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Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research. [ABSTRACT FROM AUTHOR]

Groups of sensor nodes are called as sensor networks. They transmit sensed data to base station in cooperation with one another. An efficient use of power is important for increased network lifespan. Wireless sensor networks are tiny, light in weight, battery powered sensor nodes. Various parameters like clustering overhead, lifetime of network, energy hole, FND (first node dies) LND (last end dies) are optimized in WSN with the help of various proposed hybrid unequal clustering protocol. Another issue for data gathering in WSN is the hot spot or energy hole that are formed near sink node are also addressed. Various new emerging strategies laid stress on the best optimal usage of constrain resources like energy, memory and various other processing abilities. This paper addresses a review of different research works done in this area. [ABSTRACT FROM AUTHOR]

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]

Cluster analysis is one of the most fundamental methods in data mining, and it has been widely used in economics, social sciences and computer science. However, with the rapid development of Internet technology, the volume of data required for various web applications has grown rapidly, making the traditional clustering analysis methods face technical challenges. How to obtain useful information in a large amount of data quickly and efficiently is an urgent problem in many industrial fields. With the continuous development of cloud computing technology, large amounts of data can be performed quickly and efficiently. Hadoop is an open source distributed cloud computing platform with HDFS (Digital File System) and MapReduce as its core. HDFS provides massive data storage, while MapReduce uses the MapReduce programming model to achieve parallel processing. Compared with the traditional parallel programming model, it contains basic functions such as data partitioning, task scheduling, and parallel processing, making it possible for users to develop distributed applications on their own without understanding the basics of distributed basics, thus facilitating the design of parallel programs. K-means algorithm is a typical clustering analysis method, which is widely used in industry, but the number of iterations will increase significantly due to the growth of data volume, thus reducing the efficiency of computation. In order to better apply to the cluster analysis of large-scale data, this paper firstly implements a parallelization algorithm based on MapReduce on Hadoop platform using the basic idea of MapReduce and improves the K-means algorithm for the problems of blindness and easy to fall into local optimum when selecting randomly in clusters. [ABSTRACT FROM AUTHOR]

In response to the problem that it is easy to fall into local optimum when using the traditional clustering algorithm to divide the modules, this paper improves the initialisation strategy of the NSGA2 algorithm and combines it with the FCM algorithm to propose an improved NSGA2-FCM algorithm for clustering analysis. Firstly, the FBS mapping is used to model the functional structure of the product system and identify the relationship between the product functional structures. Secondly, a correlation synthesis matrix is constructed based on the relationships between the module division drivers. Finally, the improved NSGA2-FCM algorithm is applied to cluster analysis of the product to derive the best module division scheme. The algorithm avoids falling into local optima by optimising the initialisation strategy of the NSGA2 algorithm, while using the FCM algorithm to improve the accuracy of the clustering. This allows the algorithm to explore the solution space more effectively when finding the best module partitioning solution. Finally, the effectiveness of the algorithm for module classification of light industrial equipment is verified using beer fermenters as a case study. [ABSTRACT FROM AUTHOR]

Data clustering is a machine learning method for unsupervised learning that is popular in the two areas of data analysis and data mining. The objective is to partition a given dataset into distinct clusters, aiming to maximize the similarity among data objects within the same cluster. In this paper, an improved honey badger algorithm called DELHBA is proposed to solve the clustering problem. In DELHBA, to boost the population's diversity and the performance of global search, the differential evolution method is incorporated into algorithm's initial step. Secondly, the equilibrium pooling technique is included to assist the standard honey badger algorithm (HBA) break free of the local optimum. Finally, the updated honey badger population individuals are updated with Levy flight strategy to produce more potential solutions. Ten famous benchmark test datasets are utilized to evaluate the efficiency of the DELHBA algorithm and to contrast it with twelve of the current most used swarm intelligence algorithms and k-means. Additionally, DELHBA algorithm's performance is assessed using the Wilcoxon rank sum test and Friedman's test. The experimental results show that DELHBA has better clustering accuracy, convergence speed and stability compared with other algorithms, demonstrating its superiority in solving clustering problems. [ABSTRACT FROM AUTHOR]

In recent years, clustering analysis for interval data has attracted the attention of many researchers. Nevertheless, an algorithm that can automatically determine the number of clusters, and can effectively detect the outlier intervals at the same time has not been studied so far. Therefore, in this paper, we propose a robust automatic clustering algorithm that only can automatically determine the number of clusters but also can assign the outlier intervals into separated clusters. The proposed algorithm is then applied in detecting the abnormal images consisting of the new image categories, and the images contaminated with noise. [ABSTRACT FROM AUTHOR]

In response to the problem that it is easy to fall into local optimum when using the traditional clustering algorithm to divide the modules, this paper improves the initialisation strategy of the NSGA2 algorithm and combines it with the FCM algorithm to propose an improved NSGA2-FCM algorithm for clustering analysis. Firstly, the FBS mapping is used to model the functional structure of the product system and identify the relationship between the product functional structures. Secondly, a correlation synthesis matrix is constructed based on the relationships between the module division drivers. Finally, the improved NSGA2-FCM algorithm is applied to cluster analysis of the product to derive the best module division scheme. The algorithm avoids falling into local optima by optimising the initialisation strategy of the NSGA2 algorithm, while using the FCM algorithm to improve the accuracy of the clustering. This allows the algorithm to explore the solution space more effectively when finding the best module partitioning solution. Finally, the effectiveness of the algorithm for module classification of light industrial equipment is verified using beer fermenters as a case study. [ABSTRACT FROM AUTHOR]

Path planning is receiving considerable interest in mobile robot research; however, a large number of redundant nodes are typically encountered in the path search process for large-scale maps, resulting in decreased algorithmic efficiency. To address this problem, this paper proposes a graph search path planning algorithm that is based on map preprocessing for creating a weighted graph in the map, thus obtaining a structured search framework. In addition, the reductions in the DBSCAN algorithm were analyzed. Subsequently, the optimal combination of the minPts and Eps required to achieve an efficient and accurate clustering of obstacle communities was determined. The effective edge points were then found by performing obstacle collision detection between special grid nodes. A straight-line connection or A* planning strategy was used between the effective edge points to establish a weighted, undirected graph that contained the start and end points, thereby achieving a structured search framework. This approach reduces the impact of map scale on the time cost of the algorithm and improves the efficiency of path planning. The results of the simulation experiments indicate that the number of nodes to be calculated in the search process of the weighted graph decreases significantly when using the proposed algorithm, thus improving the path planning efficiency. The proposed algorithm offers excellent performance for large-scale maps with few obstacles. [ABSTRACT FROM AUTHOR]

This paper analyzes the limitations of the current public management performance evaluation system, puts forward a public management performance evaluation model based on fuzzy clustering, and expounds on the theoretical framework and principle of this method. In addition, this paper introduces the concept and method of public management performance evaluation into the evaluation content system of government under the rule of law and realizes the combination of government under the rule of law theory and public management performance evaluation methodology. At the same time, through the analysis of the organizational system, technical system, institutional mechanism, result orientation, public satisfaction, and related index content, a public management performance evaluation index system is designed. Through macro and micro, qualitative and quantitative analysis and evaluation, the performance evaluation index system is improved, which provides an objective basis for forecasting and decision-making, control, and adjustment. The research shows that the highest accuracy of this clustering method can reach about 96%, and its accuracy is about 10% higher than that of the ID3 algorithm. It is expected that this research can make some contributions to the research of the public management performance evaluation index system. At the same time, it highlights the importance of research on performance evaluation of government ruled by law. [ABSTRACT FROM AUTHOR]

KLFCM is a clustering algorithm proposed by introducing K-L divergence into FCM, which has been widely used in the field of fuzzy clustering. Although many studies have focused on improving its accuracy and efficiency, little attention has been paid to its convergence properties and parameter selection. Like other fuzzy clustering algorithms, the output of the KLFCM algorithm is also affected by fuzzy parameters. Furthermore, some researchers have noted that the KLFCM algorithm is equivalent to the EM algorithm for Gaussian mixture models when the fuzzifier λ is equal to 2. In practical applications, the KLFCM algorithm may also exhibit self-annealing properties similar to the EM algorithm. To address these issues, this paper uses Jacobian matrix analysis to investigate the KLFCM algorithm's parameter selection and convergence properties. We first derive a formula for calculating the Jacobian matrix of the KLFCM with respect to the membership function. Then, we demonstrate the self-annealing behavior of this algorithm through theoretical analysis based on the Jacobian matrix. We also provide a reference strategy for determining the appropriate values of fuzzy parameters in the KLFCM algorithm. Finally, we use Jacobian matrix analysis to investigate the relationships between the convergence rate and different parameter values of the KLFCM algorithm. Our experimental results validate our theoretical findings, demonstrating that when selecting appropriate lambda parameter values, the KLFCM clustering algorithm exhibits self-annealing properties that reduce the impact of initial clustering centers on clustering results. Moreover, using our proposed strategy for selecting the fuzzy parameter lambda of the KLFCM algorithm effectively prevents coincident clustering results from being produced by the algorithm. [ABSTRACT FROM AUTHOR]

The vigorous development of tourism has made rural tourism a highlight of the new era. In order to better realize the classification of rural tourism features, this paper proposes a knowledge recognition algorithm based on hierarchical clustering analysis. Firstly, the rationality of the optimization of the rural tourism feature algorithm is analyzed in this paper; secondly, the rural tourism feature classification index system is constructed based on the hierarchical clustering analysis; finally, the index weights are clearly divided according to the characteristics through the hierarchical clustering analysis of the knowledge identification algorithm. According to the specialties of hierarchical clustering analysis, the criteria of the algorithm are determined, and the characteristics of rural tourism are carefully classified. Rural tourists can visit different scenic spots in high density and improve the mobility of rural tourism. The experimental results show that: through the analysis of the characteristic classification data of the Rural Tourism College in this paper, it can be seen that the average daily income of the scenic spot is higher than that of the traditional rural scenic spot. The average daily income of rural tourism has increased by more than 261,900 yuan, which has largely promoted the development trend of rural tourism in my country. It is proved that the hierarchical clustering analysis method is helpful for rational zoning and serious thinking about the characteristics of rural tourism. This paper provides a reference for promoting the classification of rural tourism. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of large datasets of mixed and attribute features under Spark using a large dataset clustering algorithm. The classical algorithm K-means based on division and the density-based clustering algorithm DPC, which has become more popular in recent years, are selected as the research objects of this paper. Secondly, the original K-means algorithm is improved by combining holdout validation and K-means++ method to address the shortcomings of the K-means algorithm that the number of class clusters K needs to be set in advance and the initial class cluster centers are chosen randomly, which leads to unstable iterations and slow convergence of clustering results. The similarity matrix will be continuously updated during the iterative process. It mainly refers to the process of dividing objects into multiple classes according to the degree of similarity between objects. After the division, the objects within the class are like each other, while the objects between the classes are different from each other. The comparison experiments of the improved algorithm before and after the MovieLens dataset are conducted to verify that the new algorithm has better performance in terms of clustering accuracy and efficiency. Again, to address the drawback that the clustering results in the DPC algorithm rely heavily on the subjective selection of the truncation distance parameter cd, and it is difficult to handle datasets with complex distribution and large density variation, the algorithm can generate the optimal cd adaptively by combining K-nearest neighbors and introducing the distance comparison quantity, which has a better performance by considering the overall and local distribution of the data. The feasibility of the improved method is verified by validating the algorithm with artificial datasets and UCI datasets as well as separation tests. Finally, the parallelized design and implementation of the improved K-means algorithm and CDPC-KNN algorithm are completed by building a Spark clustering environment, and the parallelized algorithm is verified to have much better data processing capability and be more adaptable to the clustering analysis of large-scale data by comparing algorithm string parallelism experiments. [ABSTRACT FROM AUTHOR]

Cluster analysis is a principal approach to discover unknown tumor subtypes. Innovative and effective cluster analysis methods are of great significance for tumor diagnosis and malignant tumor treatment. Existing studies on the cluster analysis of tumor gene data generally have defects in aspects such as unsatisfactory performance in clustering highdimensional and high-noise data, and insufficient accuracy in selecting cluster centers. To overcome these defects, this paper performed cluster analysis on tumor gene data based on an improved Density peaks clustering (DPC) algorithm. At first, this paper elaborated on the composition and storage format of tumor tissue samples used in the experiment, gave the tumor gene expression profile data in the matrix format, and introduced the preprocessing process of gene expression profile data. Then, this paper carried out feature selection of tumor gene expression profile data. At last, this paper innovatively divided the target gene density into two parts of K-nearest neighbor local density and neighborhood density, thereby completing the improvement of conventional DPC algorithm and expanding its application scenarios. Combining with experiment, the clustering results of the algorithm before and after introducing the idea of Approximate Nearest Neighbor (ANN) were given, which had verified the effectiveness of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a cluster-aware supervised learning (CluSL) framework, which integrates the clustering analysis with supervised learning. The objective of CluSL is to simultaneously find the best clusters of the data points and minimize the sum of loss functions within each cluster. This framework has many potential applications in healthcare, operations management, manufacturing, and so on. Because CluSL, in general, is nonconvex, we develop a regularized alternating minimization (RAM) algorithm to solve it, where at each iteration, we penalize the distance between the current clustering solution and the one from the previous iteration. By choosing a proper penalty function, we show that each iteration of the RAM algorithm can be computed efficiently. We further prove that the proposed RAM algorithm will always converge to a stationary point within a finite number of iterations. This is the first known convergence result in cluster-aware learning literature. Furthermore, we extend CluSL to the high-dimensional data sets, termed the F-CluSL framework. In F-CluSL, we cluster features and minimize loss function at the same time. Similarly, to solve F-CluSL, a variant of the RAM algorithm (i.e., F-RAM) is developed and proven to be convergent to an ∈ -stationary point. Our numerical studies demonstrate that the proposed CluSL and F-CluSL can outperform the existing ones such as random forests and support vector classification, both in the interpretability of learning results and in prediction accuracy. Summary of Contribution: Aligned with the mission and scope of the INFORMS Journal on Computing, this paper proposes a cluster-aware supervised learning (CluSL) framework, which integrates the clustering analysis with supervised learning. Because CluSL is, in general, nonconvex, a regularized alternating projection algorithm is developed to solve it and is proven to always find a stationary solution. We further generalize the framework to the high-dimensional data set, F-CluSL. Our numerical studies demonstrate that the proposed CluSL and F-CluSL can deliver more interpretable learning results and outperform the existing ones such as random forests and support vector classification in computational time and prediction accuracy. [ABSTRACT FROM AUTHOR]

In order to explore the relationship between sports and health and improve the scientific nature of sports, this paper uses cluster analysis algorithm as the basis, adopts the entropy estimation method for small sample sets to estimate the information entropy value, and improves the mutual information estimation to propose a mutual information estimation method based on entropy estimation. Moreover, this paper uses a clustering algorithm to combine sports and health intelligent diagnosis requirements to construct a system structure. The system recommends better sports suggestions to the user according to the user's physical condition, makes sports plans according to the user's health, and can also analyze the user's sports process. In addition, on the basis of demand analysis, this paper designs experiments to test the performance of the system constructed in this paper. From the experimental statistical results, it can be seen that the system constructed in this paper can basically meet the actual needs of sports and health intelligent diagnosis. At the same time, this paper proves that there is a strong correlation between sports and health. [ABSTRACT FROM AUTHOR]

The traditional neutrosophic clustering method only performs cluster analysis on the data itself, and often ignores the supervision information of data. In order to solve the above problems, a label-guided weighted semi-supervised neutrosophic clustering algorithm is proposed in the paper. On the one hand, the paired constraint information is used to construct the supervision weight coefficient and the distance measurement learning is combined to re-measure the degree of membership of the data and the cluster center; On the other hand, by minimizing the sum of squares of error between membership matrix and label matrix, the purpose of clustering results guided by label information is realized. Experiments on various data sets and comparisons with other clustering algorithms show that the new clustering algorithm can make full use of supervisory information and improve the accuracy of clustering. [ABSTRACT FROM AUTHOR]

Cluster analysis is an essential tool in data mining. Several clustering algorithms have been proposed and implemented, most of which are able to find good quality clustering results. However, the majority of the traditional clustering algorithms, such as the K-means, K-medoids, and Chameleon, still depend on being provided a priori with the number of clusters and may struggle to deal with problems where the number of clusters is unknown. This lack of vital information may impose some additional computational burdens or requirements on the relevant clustering algorithms. In real-world data clustering analysis problems, the number of clusters in data objects cannot easily be preidentified and so determining the optimal amount of clusters for a dataset of high density and dimensionality is quite a difficult task. Therefore, sophisticated automatic clustering techniques are indispensable because of their flexibility and effectiveness. This paper presents a systematic taxonomical overview and bibliometric analysis of the trends and progress in nature-inspired metaheuristic clustering approaches from the early attempts in the 1990s until today's novel solutions. Finally, key issues with the formulation of metaheuristic algorithms as a clustering problem and major application areas are also covered in this paper. [ABSTRACT FROM AUTHOR]

The recognition of malware in network traffic is an important research problem. However, existing solutions addressing this problem rely heavily on the source code and misrecognise vulnerabilities (i.e. incur a high false positive rate (FPR)) in some cases. In this paper, we initially use the K‐means clustering algorithm to extract malware patterns under user to root attacks in network traffic. Since the traditional K‐means algorithm needs to determine the number of clusters in advance and it is easily affected by the initial cluster centres, we propose an improved K‐means clustering algorithm (NIKClustering algorithm) for cluster analysis. Furthermore, we propose the use of self‐similarity and our improved clustering algorithm to recognise buffer overflow vulnerabilities for malware in network traffic. This motivates us to design and implement a recognition approach for buffer overflow vulnerabilities based on self‐similarity and our improved clustering algorithm, called Reliable Self‐Similarity with Improved K‐means Clustering (RSS‐IKClustering). Extensive experiments conducted on two different datasets demonstrate that the RSS‐IKClustering can achieve much fewer false positives than other notable approaches while increasing accuracy. We further apply our RSS‐IKClustering approach on a public dataset (Center for Applied Internet Data Analysis), which also exhibited a high accuracy and low FPR of 96% and 1.5%, respectively. [ABSTRACT FROM AUTHOR]

Clustering algorithm is a statistical method to study sample classification. With the rapid development of science and technology, people have higher and higher requirements for data classification, so there are more and more researches on clustering in modern society. Various mathematical algorithms are introduced to further improve the accuracy of clustering. Therefore, this paper proposes an improved SOM neural network algorithm to evaluate the comprehensive quality of students. SOM neural network can automatically find the internal laws and essential attributes in the samples, self-organize and adaptively change the network parameters and structure, and realize the classification of samples. Factor analysis is introduced to reduce the dimension of input layer in SOM neural network analysis, better process high-dimensional data, and improve the speed and accuracy of the algorithm. The improved SOM neural network algorithm can be used for the cluster analysis of the comprehensive quality of college students. The algorithm simulation results show that the improved neural network algorithm can intuitively evaluate the comprehensive quality of students and reflect the overall characteristics of each type of student. [ABSTRACT FROM AUTHOR]

Background: Learners in Massive Open Online Courses (MOOCs) are presented with great autonomy over their learning process. Learners must engage in self‐regulated learning (SRL) to handle this autonomy. It is assumed that learners' SRL, through monitoring and control, influences learners' behaviour within the MOOC environment (e.g., watching videos). The exact relationship between SRL and learner behaviour has however not been investigated. Objectives: We explored whether differences in SRL are related to differences in learner behaviour in a MOOC. As insight in this relationship could improve our understanding of the influence of SRL on behaviour, could help explain the variety in online learner behaviour, and could be useful for the development of successful SRL support for learners. Methods: MOOC learners were grouped based on their self‐reported SRL. Next, we used process mining to create process models of learners' activities. These process models were compared between groups of learners. Results and conclusions: Four clusters emerged: average regulators, help seekers, self‐regulators, and weak regulators. Learners in all clusters closely followed the designed course structure. However, the process models also showed differences which could be linked to differences in the SRL scores between clusters. Takeaways: The study shows that SRL may explain part of the variability in online learner behaviour. Implications for the design of SRL interventions include the necessity to integrate support for weak regulators in the course structure. Lay Description: Practitioners Notes: What is currently known:Learners' self‐regulated learning (SRL) is related to learner behaviour.Inadequate SRL may result in learner dropout in MOOCs.Learners in open online education behave in a variety of ways.What this paper adds:Most learners follow the designed course structure.Learners' SRL influences learner behaviour in a MOOC.Low SRL activity is related to adherence to the course structure.High SRL activity is related to more variety in learner behaviour.Implications for practitioners:SRL support should be integrated in the course structure.Results of process mining can inform instructional design. [ABSTRACT FROM AUTHOR]

Cluster analysis is one of the most important research issues in data mining and machine learning. To date, numerous clustering algorithms have been proposed to tackle the fixed-length vector data. In many real applications, we need to detect clusters from a set of discrete sequences in which each sequence is an ordered list of items. Due to the sequential and discrete nature, the discrete sequence clustering problem is more challenging and most of existing vector data clustering algorithms cannot be directly employed. In this paper, we present a stochastic algorithm for clustering discrete sequences. Our method first quickly generates a set of random partitions over the sequential data set and then merges these random clustering results via weighted graph construction and partition. We perform extensive empirical comparisons on real data sets to show that our method is comparable to those state-of-the-art clustering algorithms with respect to both accuracy and efficiency. • A randomized algorithm is proposed to solve the discrete sequence clustering issue. • Our algorithm provides high success rate for sequence linkage in clusters. • Experimental results demonstrate the feasibility and effectiveness of our method. [ABSTRACT FROM AUTHOR]

This case study demonstrates the application of an unsupervised topic modeling algorithm to 7,773 English-language articles published in the Library Quarterly from 1931 to 2015. The analysis of 85 years of the journal's output follows an exploratory data analysis framework to generate novel hypotheses about the history of LIS using topic modeling, a method for identifying clusters of co-occurring words within large collections of text. The paper closely examines two topics that suggest differences in gender representation in the journal to propose and support a new hypothesis regarding the historical inclusion of gendered objects of study in LIS literature. [ABSTRACT FROM AUTHOR]

In the context of the combination of industry and education, the construction of industrial colleges in vocational colleges can drive the scientific development of specialty settings in colleges and universities, and promote the way for colleges to expand students' practical teaching under the teaching of theoretical knowledge, and it is also an effective way for students to stimulate their learning enthusiasm and innovation enthusiasm. Colleges and universities can increase the direction and characteristics of specialist settings in colleges while enhancing instructors' professional level through school-business collaboration, and growing measures of talent training in colleges and universities plays a significant guiding role. The way to set up industrial colleges in vocational colleges reflects the development characteristics of talent training mode in the new era, and it is also an effective way to meet the practical training of students and the actual needs of society. It is a new school running mode of transforming productivity, cooperation, and mutual benefit, which is very worthy of promotion and development. This paper analyzes the problems existing in the construction of industrial colleges in vocational colleges in China and finds out the corresponding solutions. A path method of industrial college construction in vocational colleges based on the cluster analysis algorithm is proposed. The validity of this model is verified by experiments, which lays a foundation for the construction of industrial colleges in vocational colleges. [ABSTRACT FROM AUTHOR]

In the era of big data, text as an information reserve database is very important, in all walks of life. From humanities research to government decision-making, from precision medicine to quantitative finance, from customer management to marketing, massive text, as one of the most important information carriers, plays an important role everywhere. The text data generated in these practical problems of humanities research, financial industry, marketing, and other fields often has obvious domain characteristics, often containing the professional vocabulary and unique language patterns in these fields and often accompanied by a variety of "noise." Dealing with such texts is a great challenge for the current technical conditions, especially for Chinese texts. A clustering algorithm provides a better solution for text big data information processing. Clustering algorithm is the main body of cluster analysis, K-means algorithm with its implementation principle is simple, low time complexity is widely used in the field of cluster analysis, but its K value needs to be preset, initial clustering center random selection into local optimal solution, other clustering algorithm, such as mean drift clustering, K-means clustering in mining text big data. In view of the problems of the above algorithm, this paper first extracts and analyzes the text big data and then does experiments with the clustering algorithm. Experimental conclusion: by analyzing large-scale text data limited to large-scale and simple data set, the traditional K-means algorithm has low efficiency and reduced accuracy, and the K-means algorithm is susceptible to the influence of initial center and abnormal data. According to the above problems, the K-means cluster analysis algorithm for data sets with large data volumes is analyzed and improved to improve its execution efficiency and accuracy on data sets with large data volume set. Mean shift clustering can be regarded as making many random centers move towards the direction of maximum density gradually, that is, moving their mean centroid continuously according to the probability density of data and finally obtaining multiple maximum density centers. It can also be said that mean shift clustering is a kernel density estimation algorithm. [ABSTRACT FROM AUTHOR]

The accuracy of English pronunciation is the key index to evaluate the quality of English teaching. Correct pronunciation and smooth language flow are the expectations of every student for English learning. Aiming at the poor effect and slow speed of the original SSD (Single Shot MultiBox Detector) algorithm in English teaching pronunciation detection, this paper proposes a clustering and improved SSD algorithm for English teaching pronunciation detection and recognition. The algorithm improves the concept module to enhance the feature extraction ability of the network and improve the detection speed. Meanwhile, it integrates multiscale features to realize multilayer multiplexing and equalization of features, so as to improve the detection effect of small target sound. This algorithm extracts more features by introducing channel attention mechanism, which increases the detection accuracy while reducing computation. In order to optimize the network's ability to extract target location information, K-means clustering method is used to set the default parameters that are more in line with the characteristics of target samples. The experimental results showed that the proposed algorithm can accurately evaluate the pronunciation quality of reading aloud, so as to effectively reflect the oral English level of the reader. [ABSTRACT FROM AUTHOR]

In order to solve the problem of complex event pattern in big data and strengthen research on key technologies of the Internet of Things and computer time matching algorithms, this paper studies the problem based on Hadoop clustering algorithm. Firstly, based on the subtype attribute of event type, the maximum value is selected as the final attribute value of the event after weighting the event. Secondly, cluster analysis is conducted on the Internet of Things flow dataset through the relationship between complex events. Finally, the simulation test is carried out on the simulation dataset of complex event relationship, and the simulation test of clustering algorithm comparison is carried out. The experimental results show that the clustering accuracy of different datasets is above 85%, and the clustering accuracy of causality reaches 96.07% when the dataset is 5G. Therefore, the algorithm proposed has high feasibility, good stability, and high speed and effectiveness for complex event processing. This study has certain practical significance for solving the problem of complex event pattern in big data. [ABSTRACT FROM AUTHOR]

The IoT and Artificial intelligence, the amount of information generated on the Web site is increasing. The rise of the Hadoop distributed cloud computing platform (HDCCP) makes it possible to use multiple computing nodes for parallel computing to solve the performance problems of traditional serial algorithms. The purpose of this paper is to study data design based on cloud computing and improved k-means algorithm (KMA). This paper deeply researches Hadoop distributed cloud computing platform and clustering algorithm and other related technologies, and designs and implements a cluster analysis system (CAS) based on HP. And through an in-depth analysis of the problems existing in the KMA, an improved scheme based on the HDP is designed. The experimental environment was conFig.d with the cluster analysis system implemented. Finally, the improved KMPA was tested experimentally from four directions: convergence speed, acceleration ratio, initialization sampling rate, and accuracy rate. We can see the experimental results that the CAS based on the HDCCP designed in this paper can provide efficient and configurable cluster analysis services. In this paper, the correct rate is 90.7%. [ABSTRACT FROM AUTHOR]

Data analysis becomes more important since rapid technology developments. In data analysis, data clustering is one of the very useful approaches. It can reveal important information hiding inside the dataset by organizing the instances based on their similarity. The objectives of data clustering are maximizing dissimilarity between clusters and minimizing dissimilarity within clusters. In order to construct a good clustering results, many clustering algorithms have been proposed, including the metaheuristic-based clustering algorithms. Recently, a new metaheuristic algorithm named gradient evolution has been proposed. This algorithm shows a good performance on solving the optimization problems. Therefore, this paper employs this GE algorithm for solving the clustering problem. In order to obtain a better clustering result, this paper considers multi-objective clustering instead of single-objective clustering. In this paper, the original GE algorithm is improved so then it is suitable for the multi-objective problem. The proposed modification includes the procedure for vector updating and jumping which involves Pareto rank assignment. In addition, it also employs K-means algorithm to provide the final clustering result. The proposed algorithm is verified using some benchmark datasets. It is also compared with some other multi-objective metaheuristic-based clustering algorithms. The experimental results show that the proposed algorithm can obtain better results than other metaheuristic-based algorithms. [ABSTRACT FROM AUTHOR]

How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain "outliers," and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person's understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation. [ABSTRACT FROM AUTHOR]

This paper aims to study the internal aerodynamics of a Miller cycle gasoline engine. This cycle uses an over-expanded stroke to recover more work, for the same amount of fuel, than the classical Otto cycle. To achieve this, the effective compression stroke is made smaller than the expansion one by closing the intake valves prematurely. The in-cylinder tumble flow then stops being driven by the valve jet before the piston reaches the bottom dead center, which disturbs the movement. The flow is observed here on a motored single-cylinder transparent engine coupled to a dual-PIV system. This system allows two velocity fields per engine cycle to be measured. The temporal evolution of the flow structures could thus be followed, while keeping the high number of instantaneous fields measured by the traditional PIV systems. The data are then well adapted to statistical analysis. A clustering analysis method, using the k-means algorithm and adapted to dual-PIV data, is described here. The first part presents a method defining a criterion on the instantaneous center of rotation of the tumble motion that can improve the rotation rates by 17%. A second analysis classifies the velocity fields into three groups of strong, medium and weak rotation intensity. The evolution of the characteristic quantities of each group shows that the movements with high rotation rates, created upstream of the compression phase, can give high turbulence rates close to the top dead center of the piston. [ABSTRACT FROM AUTHOR]

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods. [ABSTRACT FROM AUTHOR]

Clustering is an important data mining technique described as unsupervised learning. Till date, many single-objective clustering algorithms have been developed on the basis of swarm intelligence and evolutionary techniques. It is noticed that these clustering algorithms provide better solutions for clustering problems, but sometimes, these solutions seem to be biased and also not appropriate for the problem with geometrical shapes datasets. In turn, performance of the clustering algorithms can be degraded. One of the possible solutions is to adopt multi-objective approach instead of single objective. In multi-objective approach, more than one objective functions can be considered for solving the clustering problems and these functions are conflicted in nature. Further, in multi-objective approach, Pareto-optimal solutions can be generated for improving the clustering performance. Hence, this paper presents a multi-objective clustering algorithm based on vibrating particle system (VPS) for effective cluster analysis, called MOVPS. This work considers intra-cluster variance and connectedness as objective functions, and VPS algorithm is used for optimizing the aforementioned objectives to obtain good clustering results. The performance of MOVPS algorithm is tested over a set of benchmark datasets and validated by comparing clustering results with various multi-objective and single-objective clustering algorithms from the literature. The simulation results illustrate the effectiveness of the MOVPS algorithm based on F-measure, coverage, distribution, convergence, non-dominating vector generation and intra-cluster distance measures. The simulation results showed that the proposed MOVPS algorithm enhances the clustering results significantly in comparison with existing multi-objective and single-objective clustering algorithms. [ABSTRACT FROM AUTHOR]

The arrangement of atoms in the microstructures of alloys plays a key role in defining their physical and functional properties. These microstructures are studied to detect deviations from the expected homogeneous distribution using frequency distribution tests and cluster detection algorithms on reconstructions obtained from atom probe microscopy. However, it is challenging to evaluate and validate the performance of these algorithms using the reconstructed data because of the experimental limitations of the atom probe data itself. Low detector efficiency and reconstructional artifacts may lead to loss of existing clusters, or introduce heterogeneities that do not reflect the original arrangement. Additionally, retrieving atom probe data is a time and cost-prohibitive process. These challenges call for the use of simulated datasets with known concentrations and distributions to evaluate algorithms that analyze distributions or detect clusters. In this paper, we extend the existing methodology to simulate the creation of solute clusters in very dilute solutions for any solution. We propose methods to create denser and wider clusters in any solution irrespective of the concentration. Three novel solute cluster simulation techniques incremental, hierarchical and gravitational clustering are presented and compared in this paper. The simulated datasets can be used to validate and compare the performance of frequency distribution or cluster discovery algorithms. We compare the cluster analysis results obtained from the single linkage algorithm and DBSCAN to highlight the utility of these datasets. [ABSTRACT FROM AUTHOR]

Clustering is a traditional data mining problem that has attracted researchers from different disciplines because its solution can be applied to many useful problems in our daily life. Since the era of big data is coming, how to "reduce the computing time" of an "effective clustering algorithm" has been a promising research issue in recent years. Thus, this paper presents an effective clustering algorithm, by using the so-called searched information to determine later search directions, and then has it implemented on Spark to accelerate its response time for analyzing large-scale datasets. Simulation results show that the proposed algorithm provides a better result than the other clustering algorithms compared in this paper because it is less sensitive to the initial solutions. The simulation results further show that cloud computing platform is capable of enhancing the performance of the proposed algorithm. • This study presents a high-performance clustering algorithm based on searched experiences. • The results show that it is able to find a better result than traditional clustering algorithms. • We apply it to Spark to show its possibility to accelerate the response time on a cloud system. [ABSTRACT FROM AUTHOR]

Bridge structures deteriorate due to various factors, and their maintenance can be made more efficient by utilizing relevant information. This paper describes an algorithm called Self-Organizing Map-based Cluster Merging (SOMCM) using a multi-dimensional matrix composite neural network, and a surface image identification system. The algorithm is designed to investigate the relevance between the main components of bridges and their types of deterioration. 6140 records on bridge maintenance were collected for all bridges in the Taoyuan region of Taiwan in 2021. The SOMCM algorithm involves finding the winner neuron through merging processes. 61 clusters were merged into 8 clusters after a predetermined number of iterations. Clustering analysis of the final 8 clusters revealed 9 major bridge maintenance association rules. Follow-up studies can apply the algorithm to integrate more technologies including GIS coordinates, material availability, real-time traffic conditions, and weather information to be of benefit to engineering practitioners. • The study is to develop a new algorithm, SOM-based Cluster Merging (SOMCM). • The paper is to investigate the relevance for bridge main components and their deterioration types. • The initial result involves 61 clusters being merged to 8 clusters with the predetermined iterations. • The clustering analysis for the final 8 clusters shows 9 major bridge maintenance association rules. [ABSTRACT FROM AUTHOR]

The explicit tau-leaping procedure attempts to speed up the stochastic simulation of a chemically reacting system by approximating the number of firings of each reaction channel during a chosen time increment τ as a Poisson random variable. Since the Poisson random variable can have arbitrarily large sample values, there is always the possibility that this procedure will cause one or more reaction channels to fire so many times during τ that the population of some reactant species will be driven negative. Two recent papers have shown how that unacceptable occurrence can be avoided by replacing the Poisson random variables with binomial random variables, whose values are naturally bounded. This paper describes a modified Poisson tau-leaping procedure that also avoids negative populations, but is easier to implement than the binomial procedure. The new Poisson procedure also introduces a second control parameter, whose value essentially dials the procedure from the original Poisson tau-leaping at one extreme to the exact stochastic simulation algorithm at the other; therefore, the modified Poisson procedure will generally be more accurate than the original Poisson procedure. [ABSTRACT FROM AUTHOR]

This paper proposes an Automatic Clustering algorithm for Interval data using the Genetic algorithm (ACIG). In this algorithm, the overlapped distance between intervals is applied to determining the suitable number of clusters. Moreover, to optimize in clustering, we modify the Davies & Bouldin index, and to improve the crossover, mutation, and selection operators of the original genetic algorithm. The convergence of ACIG is theoretically proved and illustrated by the numerical examples. ACIG can be implemented effectively by the established Matlab procedure. Through the experiments on data sets with different characteristics, the proposed algorithm has shown the outstanding advantages in comparison to the existing ones. Recognizing the images by the proposed algorithm gives the potential in real applications of this research. [ABSTRACT FROM AUTHOR]

Clustering analysis is a popular data analysis technology that has been successfully applied in many fields, such as pattern recognition, machine learning, image processing, data mining, computer vision and fuzzy control. Clustering analysis has made great progress in these fields. The purpose of clustering analysis is to classify data according to their intrinsic attributes such that data that have the same characteristics are in the same class and data that differ are in different classes. Currently, the k-means clustering algorithm is one of the most commonly used clustering methods because it is simple and easy to implement. However, its performance largely depends on the initial solution, and it easily falls into locally optimal solutions during the execution of the algorithm. To overcome the shortcomings of k-means clustering, many scholars have used meta-heuristic optimization algorithms to solve data clustering problems and have obtained satisfactory results. Therefore, in this paper, a selfish herd optimization algorithm based on the simplex method (SMSHO) is proposed. In SMSHO, the simplex method replaces mating operations to generate new prey individuals. The incorporation of the simplex method increases the population diversity of algorithm, thereby improving the global searching ability of algorithm. Twelve clustering datasets are selected to verify the performance of SMSHO in solving clustering problems. The SMSHO is compared with ABC, BPFPA, DE, k-means, PSO, SMSSO and SHO. The experimental results show that SMSHO has faster convergence speed, higher accuracy and higher stability than the other algorithms. [ABSTRACT FROM AUTHOR]

DBSCAN is a famous density-based clustering algorithm that can discover clusters with arbitrary shapes without the minimal requirements of domain knowledge to determine the input parameters. However, DBSCAN is not suitable for databases with different local-density clusters and is also a very time-consuming clustering algorithm. In this paper, we present a quantum mutual MinPts-nearest neighbor graph (MMNG)-based DBSCAN algorithm. The proposed algorithm performs better on databases with different local-density clusters. Furthermore, the proposed algorithm has a dramatic increase in speed compared to its classic counterpart. [ABSTRACT FROM AUTHOR]

As a fundamental task in cluster analysis, community detection is crucial for the understanding of complex network systems in many disciplines such as biology and sociology. Recently, due to the increase in the richness and variety of attribute information associated with individual nodes, detecting communities in attributed graphs becomes a more challenging problem. Most existing works focus on the similarity between pairwise nodes in terms of both structural and attribute information while ignoring the higher-order patterns involving more than two nodes. In this paper, we explore the possibility of making use of higher-order information in attributed graphs to detect communities. To do so, we first compose tensors to specifically model the higher-order patterns of interest from the aspects of network structures and node attributes, and then propose a novel algorithm to capture these patterns for community detection. Extensive experiments on several real-world datasets with varying sizes and different characteristics of attribute information demonstrated the promising performance of our algorithm. [ABSTRACT FROM AUTHOR]

Evolutionary algorithm (EA) incorporating with k-means local search operator represents an important approach for cluster analysis. In the existing EA approach, however, the k-means operators are usually directly employed on the individuals and generally applied with fixed intensity as well as frequency during evolution, which could significantly limit their performance. In this paper, we first introduce a hybrid EA based clustering framework such that the frequency and intensity of k-means operator could be arbitrarily configured during evolution. Then, an adaptive strategy is devised to dynamically set its frequency and intensity according to the feedback of evolution. Further, we develop an opposite search strategy to implement the proposed adaptive k-means operation, thus appropriately exploring the search space. By incorporating the above two strategies, a memetic algorithm with adaptive and opposite k-means operation is finally proposed for data clustering. The performance of the proposed method has been evaluated on a series of data sets and compared with relevant algorithms. Experimental results indicate that our proposed algorithm is generally able to deliver superior performance and outperform related methods. [ABSTRACT FROM AUTHOR]

Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method's sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm's stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall. [ABSTRACT FROM AUTHOR]

Abstract Recent years have witnessed the rapid development of knowledge bases (KBs) such as WordNet, Yago and DBpedia, which are useful resources in AI-related applications. However, most of the existing KBs are suffering from incompleteness and manually adding knowledge into KBs is inefficient. Therefore, automatically mining knowledge becomes a critical issue. To this end, in this paper, we propose to develop a model (S 2 AMT) to extract knowledge triples, such as < Barack Obama, wife, Michelle Obama > , from the Internet and add them to KBs to support many downstream applications. Particularly, because the seed instances 1 1 In this paper, seed instances refer to labeled positive instances. for every relation is difficult to obtain, our model is capable of mining knowledge triples with limited available seed instances. To be more specific, we treat the knowledge triple mining task for each relation as a single task and use multi-task learning (MTL) algorithms to solve the problem, because MTL algorithms can often get better results than single-task learning (STL) ones with limited training data. Moreover, since finding proper task groups is a fatal problem in MTL which can directly influences the final results, we adopt a clustering algorithm to find proper task groups to further improve the performance. Finally, we conduct extensive experiments on real-world data sets and the experimental results clearly validate the performance of our MTL algorithms against STL ones. Highlights • We propose S 2 AMT to solve the problem of KTM with limited seed instances. • Our framework obtain better performance using MTL methods. • Our framework jointly use labeled and unlabeled instances during the training stage. • We give a fast method to find related tasks to further improve the performance. • Our work provides a new perspective for KTM when having limited seed instances. [ABSTRACT FROM AUTHOR]

Highlights • The paper proposes a data-driven algorithm to deal with the block-sparsity recovery problem. • A recurrent neural network with LSTM block is introduced to capture the intra-structure of signals. • The algorithm is no need for the prior knowledge of partition patterns. Abstract Several researches on block-sparsity recovery have been recently carried. Block-sparse signals with nonzero elements occurring in clusters arise naturally in many practical applications. However, the priori knowledge of block partitions is usually unavailable in reality, which increases the difficulty of recovery greatly. At the meantime, deep learning methods have been developed rapidly in last few years as a kind of data-driven methods. In this paper, we propose a novel approach based on recurrent neural networks for recovery of block-sparse signals with unknown cluster patterns. In our work, the recurrent neural network containing the long short-term memory is introduced to acquire the spatial correlations between nonzero elements of block-sparse signals. Extensive experiments both on synthetic data and real-world data show that the proposed method outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract Clustering analysis is to classify elements into categories based on their similarity. Clustering by fast search and find of density peaks (CFSFDP) has been proven to be an effective and novel algorithm, which identifies the centers of clusters with density maxima. However, the performance of CFSFDP is quite sensitive to the estimation of densities, that is exactly the selection of the cutoff distance (d c). In a conventional way, the selection of d c is based on subjective experience. It meets difficulties in finding an appropriate d c , especially for detecting nonspherical clusters, because CFSFDP cannot perform well when there are more than one density peak for one cluster. Besides, another barrier of applying CFSFDP is that manual interaction is always required for making an effective selection of cluster centers. In this paper, a new density-based clustering algorithm, clustering by defining and merging candidates of cluster centers via independence and affinity (CDMC-IA), is proposed. With its strategy, an appropriate value of cutoff distance d c can be well suggested and the robustness of the method itself is enhanced. Moreover, CDMC-IA introduces a new quantity independence to sort and select cluster centers, instead of human based selection from decision graph. Another quantity affinity is also introduced, which well handles multiple density peaks existing in one cluster and is able to assign each data point to the its targeted cluster. The performance of applying conventional clustering methods to benchmark datasets will be compared with the proposed method in this paper. [ABSTRACT FROM AUTHOR]