Search

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]

The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment. [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]

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]

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]

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]

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]

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]

The software of digital PCR system lacked the function for classifying droplets of multiplex experiments. This paper proposed a grid regional DBSCAN clustering algorithm. Firstly, it mapped data to the gridded space and established gridded indexes ;then it calculated weights of grids and performing deep search expansion;finally,it mapped the clustering results in gridded space back to the data space . Tests on artificial datasets show that the proposed algorithm is able to identify the sample points in edge area and of high efficiency. On this basis, it proposed a classification method of digital PCR droplets based on grid regional DBSCAN clustering. It used both the proposed method and existing methods to classify digital PCR droplets. The results show that this method is a convenient and accurate method for classification of digital PCR droplets. Therefore, the proposed method is suitable for classification of digital PCR droplets. [ABSTRACT FROM AUTHOR]

This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved differential privacy protecting K-means clustering algorithm was raised in this paper. The improved algorithm uses the contour coefficients to quantitatively evaluate the clustering effect of each iteration and add different noise to different clusters. In order to be adapted to the huge number of data, this paper provides an algorithm design in MapReduce Framework. Experimental finding shows that the new algorithm improves the availability of the algorithm clustering results under the condition of ensuring individual privacy without significantly increasing its operating time. [ABSTRACT FROM AUTHOR]

This paper proposes a new unsupervised learning algorithm called jackknife entropy-based clustering algorithm for grouping families of probability density functions (pdfs). The fitness function is used to choose the best threshold values of similarity in the proposed algorithm. We demonstrate the correctness and robustness of the proposed algorithm on a synthetic data set. Finally, we apply the algorithm to texture clustering. [ABSTRACT FROM AUTHOR]

The paper presents the results of the development and testing of the system for constructing neurosemantic maps of the human brain based on fMRI data. The method of modeling the reaction of brain regions to the presentation of stimulus material has been developed and implemented, and neurosemantic maps in the gray matter of the human brain have been visualized. Personally and emotionally significant texts in Russian were used as a testing stimulus material. The results of semantic clustering analysis of the obtained model at individual and group level will be considered in the study. [ABSTRACT FROM AUTHOR]

Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment. [ABSTRACT FROM AUTHOR]

One of the first decisions to be made when building a mineral resource model is the definition of geological/geostatistical domains. Cluster analysis is a set of techniques in machine learning that can be especially suited for this matter. In order to compare different approaches, two clustering algorithms were investigated in this study: k-means and the dual-space clustering algorithm. Choosing the most appropriate method and the number of clusters can be challenging and some metrics are needed to support these decisions, including the validation of the spatial distribution of the clusters, which is not always appropriately discussed in the literature. We introduce the use of correlograms of the indicators for that matter. Although clustering techniques can be robust for an application in resource modelling, expert knowledge is still necessary when applying cluster analysis to resource modeling, since final decisions should not be based solely on statistical indexes, but also on experience. In this paper, the proposed methodology was tested in a three-dimensional dataset related to a phosphate/titanium deposit. [ABSTRACT FROM AUTHOR]

Fuzzy C-means (FCM) is a classical algorithm of cluster analysis which has been applied to many fields including artificial intelligence, pattern recognition, data aggregation and their applications in software engineering, image processing, IoT, etc. However, it is sensitive to the initial value selection and prone to get local extremum. The classification effect is also unsatisfactory which limits its applications severely. Therefore, this paper introduces the artificial-fish-swarm algorithm (AFSA) which has strong global search ability and adds an adaptive mechanism to make it adaptively adjust the scope of visual value, improves its local and global optimization ability, and reduces the number of algorithm iterations. Then it is applied to the improved FCM which is based on the Mahalanobis distance, named as adaptive AFSA-inspired FCM(AAFSA-FCM). The optimal solution obtained by adaptive AFSA (AAFSA) is used for FCM cluster analysis to solve the problems mentioned above and improve clustering performance. Experiments show that the proposed algorithm has better clustering effect and classification performance with lower computing cost which can be better to apply to every relevant area, such as IoT, network analysis, and abnormal detection. [ABSTRACT FROM AUTHOR]

This paper argues that methods used for the classification and measurement of online education are not neutral and objective, but involved in the creation of the educational realities they claim to measure. In particular, the paper draws on material semiotics to examine cluster analysis as a 'performative device' that, to a significant extent, creates the educational entities it claims to objectively represent through the emerging body of knowledge of Learning Analytics (LA). It also offers a more critical and political reading of the algorithmic assemblages of LA, of which cluster analysis is a part. Our argument is that if we want to understand how algorithmic processes and techniques like cluster analysis function as performative devices, then we need methodological sensibilities that consider critically both their political dimensions and their technical-mathematical mechanisms. The implications for critical research in educational technology are discussed. [ABSTRACT FROM AUTHOR]

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date. [ABSTRACT FROM PUBLISHER]

Currently, the athletes' post-match scores are mostly manual methods, and artificial intelligence is still less used in athletes' post-match scores. Based on this, this study is based on machine learning algorithms and combined with athletes' scores for analysis. At the same time, this study uses the reptile technology to conduct real-time mining of athletes' data and proposes a model-based regression algorithm in the construction of scoring algorithm. Moreover, based on the actual situation, a comprehensive model combining clustering and regression is proposed. In addition, in order to study the validity of the model, this paper designs a performance simulation test, compares the proposed algorithm model with the traditional algorithm model, and collects relevant experimental data and draws the corresponding statistical graph. The experimental results show that the combination of clustering and regression can improve the model's effect and the results are like the expert scores, which verifies the practicality of the proposed algorithm and provides a theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. [ABSTRACT FROM AUTHOR]

Since drug-target data have neither class labels nor the cluster number information, they are not suitable for clustering algorithms that require predefined parameters determined by comparing clustering results with real class labels. Density peaks clustering (DPC) is a density-based clustering algorithm that can determine the number of clusters without requiring class labels. However, the predefined cutoff distance of local density limits its wide application. Therefore, this paper proposes an improved local density method based on a cutoff distance sequence that overcomes the limitations of DPC and can be successful applied to drug-target data. We also introduce multiple-dimensional scaling based on drug and target similarity and perform intuitive graph analysis of the two most significant differentiation features. Drugs of the Enzyme, GPCR, Ion Channel, and Nuclear Receptor 4 standard datasets are identified as 6, 6, 3, and 5 clusters by an improved algorithm, respectively, and similarly, their targets are identified be 5, 5, 8, and 4 clusters. Drug-target data clustering results of the improved algorithm are more reasonable than the results of the fast K-medoids and hierarchical clustering algorithms. [ABSTRACT FROM AUTHOR]

Clustering by fast search and find of density peaks (herein called FDPC), as a recently proposed density-based clustering algorithm, has attracted the attention of many researchers since it can recognize arbitrary-shaped clusters. In addition, FDPC needs only one parameter d c and identifies the number of clusters by decision graph. Nevertheless, it is not clear how to find a proper d c for a given data set and such a perfect parameter may not exist in practice for the multi-scale data set. In this paper, we proposed a modified PageRank algorithm to compute the local density for each data point which is more robust than Gaussian kernel and cutoff method. Besides, FDPC yields poor results on the random distribution data sets since there may be several maxima for one cluster. To solve this problem, we proposed an improved minimax similarity method. Comparing our proposed approach with FDPC on some artificial and real-life data sets, the experimental results indicate that our proposed approach outperforms FDPC in terms of accuracy. [ABSTRACT FROM AUTHOR]

Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error. [ABSTRACT FROM AUTHOR]

Sensor network intrusion detection has attracted extensive attention. However, previous intrusion detection methods face the highly imbalanced attack class distribution problem, and they may not achieve a satisfactory performance. To solve this problem, we propose a new intrusion detection algorithm based on normalized cut spectral clustering for sensor network in this paper. The main aim is to reduce the imbalance degree among classes in an intrusion detection system. First, we design a normalized cut spectral clustering to reduce the imbalance degree between every two classes in the intrusion detection data set. Second, we train a network intrusion detection classifier on the new data set. Finally, we do extensive experiments and analyze the experimental results in detail. Simulation experiments show that our algorithm can reduce the imbalance degree among classes and reserves the distribution of the original data on the one hand, and improve effectively the detection performance on the other hand. [ABSTRACT FROM AUTHOR]

• A new CDC algorithm. • 6 indicators to evaluate the results. • 26 experiments had been conducted. A variety of algorithms have recently emerged in the field of cluster analysis. Consequently, based on the distribution nature of the data, an appropriate algorithm can be chosen for the purpose of clustering. It is difficult for a user to decide a priori which algorithm would be the most appropriate for a given dataset. Algorithms based on graphs provide good results for this task. However, these algorithms are vulnerable to outliers with limited information about edges contained in the tree to split a dataset. Thus, in several fields, the need for better clustering algorithms increases and for this reason utilizing robust and dynamic algorithms to improve and simplify the whole process of data clustering has become an urgent need. In this paper, we propose a novel distance-based clustering algorithm called the critical distance clustering algorithm. This algorithm depends on the Euclidean distance between data points and some basic mathematical statistics operations. The algorithm is simple, robust, and flexible; it works with quantitative data that are real-valued, not qualitative, and categorical with different dimensions. In this work, 26 experiments are conducted using different types of real and synthetic datasets taken from different fields. The results prove that the new algorithm outperforms some popular clustering algorithms such as MST-based clustering, K-means, and Dbscan. Moreover, the algorithm can precisely produce more reasonable clusters even when the dataset contains outliers and without specifying any parameters in advance. It also provides a number of indicators to evaluate the established clusters and prove the validity of the clustering. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach for learning node embeddings for weighted undirected networks. We perform a random walk on the network to extract the latent structural information and perform node embedding learning under a similarity-based framework. Unlike previous works, we apply a different criterion to capture the proximity information between nodes in a network, and use it for improved modeling of similarities between nodes. We show that the mean commute time (MCT) between two nodes, defined as the average time a random walker takes to reach a target node and return to the source, plays a crucial role in quantifying the actual degree of proximity between two nodes of the network. We then introduce a novel definition of a similarity matrix that is based on the pair-wise mean commute time captured, which enables us to adequately represent the connection of similar nodes. We utilize pseudoinverse of the Laplacian matrix of the graph for calculating such a proximity measure, capturing rich structural information out of the graph for learning more adequate node representations of a network. The results of different experiments on three real-world networks demonstrate that our proposed method outperforms existing related efforts in classification, clustering, visualization as well as link prediction tasks. [ABSTRACT FROM AUTHOR]

• A novel combination of offline deceleration profile segmentation and online risk level classification. • Deceleration curves from critical evasive braking were clustered into different risk levels using spectrum clustering. • Risk classification rules at critical braking onset were extracted according to deceleration curve clusters. • Promising in balancing the objectives of avoiding collision and reducing interference with driver's normal driving. Measuring risk is critical for collision avoidance. The paper aims to develop an online risk level classification algorithm for forward collision avoidance systems. Assuming risk levels are reflected by braking profiles, deceleration curves from critical evasive braking events from the Virginia "100-car" database were first extracted. The curves are then clustered into different risk levels based on spectrum clustering, using curve distance and curve changing rate as dissimilarity metrics among deceleration curves. Fuzzy logic rules of safety indicators at critical braking onset for risk classification were then extracted according to the clustered risk levels. The safety indicators include time to collision, time headway, and final relative distance under emergency braking, which characterizes three kinds of uncertain critical conditions respectively. Finally, the obtained fuzzy risk level classification algorithm was tested and compared with other Automatic Emergency Braking (AEB) algorithms under Euro-NCAP testing scenarios in simulation. Results show the proposed algorithm is promising in balancing the objectives of avoiding collision and reducing interference with driver's normal driving compared with other algorithms. [ABSTRACT FROM AUTHOR]

As the location trajectory contains more spatial-temporal information about the user, it will be even dangerous for jeopardizing the privacy of the user. In order to cope with the correlation, an algorithm that utilizes the query division had been proposed. In this algorithm, random blocks of query context was used, so as the adversary was obfuscated and difficult to correlate the real result. However, this algorithm fails to dispose the size of each query block, as once same size blocks were obtained by the adversary continuously, so the adversary can regard them as blocks from the same query context, and then obtains the query context to correlate the discrete locations. In view of above conditions, in this paper we propose a fine granularity block division algorithm based on the conception of granularity measurement as well as granularity layer division, so with the help of collaborative users the location privacy of the user will be protected. In this algorithm, the query context will be divided into fine granularity size of information blocks that difficult to be distinguished with others, and then these blocks will be exchanged with other collaborative users to eliminate the difference in block size. In addition, as each block is divided into fine granularity size, the adversary will be difficult to correlate the discrete locations into location trajectory, so the location privacy will be protected. At last, through security analysis and experimental verification, this granularity indistinguishable algorithm is analyzed and verified at both theoretical and practical levels, which further demonstrate the superiority of the proposed algorithm compared with other similar algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a human action recognition method using HOIRM (histogram of oriented interest region motion) feature fusion and a BOW (bag of words) model based on AP (affinity propagation) clustering. First, a HOIRM feature extraction method based on spatiotemporal interest points ROI is proposed. HOIRM can be regarded as a middle-level feature between local and global features. Then, HOIRM is fused with 3D HOG and 3D HOF local features using a cumulative histogram. The method further improves the robustness of local features to camera view angle and distance variations in complex scenes, which in turn improves the correct rate of action recognition. Finally, a BOW model based on AP clustering is proposed and applied to action classification. It obtains the appropriate visual dictionary capacity and achieves better clustering effect for the joint description of a variety of features. The experimental results demonstrate that by using the fused features with the proposed BOW model, the average recognition rate is 95.75% in the KTH database, and 88.25% in the UCF database, which are both higher than those by using only 3D HOG+3D HOF or HOIRM features. Moreover, the average recognition rate achieved by the proposed method in the two databases is higher than that obtained by other methods. [ABSTRACT FROM AUTHOR]

How can we extract hidden relations from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is an important tool for this purpose. Designing an accurate and efficient CMTF method has become more crucial as the size and dimension of real-world data are growing explosively. However, existing methods for CMTF suffer from lack of accuracy, slow running time, and limited scalability. In this paper, we propose S3CMTF, a fast, accurate, and scalable CMTF method. In contrast to previous methods which do not handle large sparse tensors and are not parallelizable, S3CMTF provides parallel sparse CMTF by carefully deriving gradient update rules. S3CMTF asynchronously updates partial gradients without expensive locking. We show that our method is guaranteed to converge to a quality solution theoretically and empirically. S3CMTF further boosts the performance by carefully storing intermediate computation and reusing them. We theoretically and empirically show that S3CMTF is the fastest, outperforming existing methods. Experimental results show that S3CMTF is up to 930× faster than existing methods while providing the best accuracy. S3CMTF shows linear scalability on the number of data entries and the number of cores. In addition, we apply S3CMTF to Yelp rating tensor data coupled with 3 additional matrices to discover interesting patterns. [ABSTRACT FROM AUTHOR]

Clustering large and complex data sets whose partitions may adopt arbitrary shapes remains a difficult challenge. Part of this challenge comes from the difficulty in defining a similarity measure between the data points that captures the underlying geometry of those data points. In this paper, we propose an algorithm, DCG++ that generates such a similarity measure that is data-driven and ultrametric. DCG++ uses Markov Chain Random Walks to capture the intrinsic geometry of data, scans possible scales, and combines all this information using a simple procedure that is shown to generate an ultrametric. We validate the effectiveness of this similarity measure within the context of clustering on synthetic data with complex geometry, on a real-world data set containing segmented audio records of frog calls described by mel-frequency cepstral coefficients, as well as on an image segmentation problem. The experimental results show a significant improvement on performance with the DCG-based ultrametric compared to using an empirical distance measure. [ABSTRACT FROM AUTHOR]

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm. [ABSTRACT FROM AUTHOR]

Since multi-view data are available in many real-world clustering problems, multi-view clustering has received considerable attention in recent years. Most existing multi-view clustering methods learn consensus clustering results but do not make full use of the distinct knowledge in each view so that they cannot well guarantee the complementarity across different views. In this paper, we propose a Distinction based Consensus Spectral Clustering (DCSC), which not only learns a consensus result of clustering, but also explicitly captures the distinct variance of each view. It is by using the distinct variance of each view that DCSC can learn a clearer consensus clustering result. In order to optimize the introduced optimization problem effectively, we develop a block coordinate descent algorithm which is theoretically guaranteed to converge. Experimental results on real-world data sets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The clustering of time series has attracted growing research interest in recent years. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation, in this paper, we study clustering methods applicable to time series with a general and dependent structure. We propose a copula-based distance to measure dissimilarity among time series and consider an estimator for it, where the strong consistency of the estimator is guaranteed. Once the pairwise distance matrix for time series has been obtained, we apply a hierarchical clustering algorithm to cluster the time series and ensure its consistency. Numerical studies, including a large number of simulations and analysis of practical data, show that our method performs well. [ABSTRACT FROM AUTHOR]

The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets. First, the clustering problem is formulated as a nonsmooth optimization problem. Then the limited memory bundle method [Haarala et al., 2007] is modified and combined with an incremental approach to design a new clustering algorithm. The algorithm is evaluated using real world data sets with both the large number of attributes and the large number of data points. It is also compared with some other optimization based clustering algorithms. The numerical results demonstrate the efficiency of the proposed algorithm for clustering in very large data sets. [ABSTRACT FROM AUTHOR]

According to the standard fuzzy C-means clustering algorithm performed poor in the clustering effect during the clustering process. This paper presents an objective function optimization based on the attribute weighted and the objective function optimization. Firstly, use a little prior knowledge as the labeled sample. These calibrated samples information are used as the prior knowledge, and then use the self-creating method and the statistical characteristics based on the data to optimize the attribute weights in FCM algorithm, and then introduce the kernel function to improve the search ability of the fuzzy C-means clustering algorithm, simplify both the clustering center and membership matrix with lagrange multiplier approach. The simulation experiment shows that, contrast to the original algorithm and K-means clustering algorithm, the fuzzy C-means clustering algorithm optimized by the attribute weighted method presented in this paper has a better clustering effect. [ABSTRACT FROM AUTHOR]

Automatic Vehicles Counting and Recognizing (AVCR) is a very challenging topic in transport engineering having important implications for the modern transport policies. Implementing a computer-assisted AVCR in the most vital districts of a country provides a large amount of measurements which are statistically processed and analyzed, the purpose of which is to optimize the decision-making of traffic operation, pavement design, and transportation planning. Since, the advent of computer vision technology, video-based surveillance of road vehicles has become a key component in developing autonomous intelligent transportation systems. In this context, this paper proposes a Pattern Recognition system which employs an unsupervised clustering algorithm with the objective of detecting, counting and recognizing a number of dynamic objects crossing a roadway. This strategy defines a virtual sensor, whose aim is similar to that of an inductive-loop in a traditional mechanism, i.e. to extract from the traffic video streaming a number of signals containing anarchic information about the road traffic. Then, the set of signals is filtered with the aim of conserving only motion’s significant patterns. Resulted data are subsequently processed by a statistical analysis technique so as to estimate and try to recognize a number of clusters corresponding to vehicles. Finite mixture models fitted by the EM algorithm are used to assess such clusters, which provides important information about the traffic such as the instantaneous number of vehicles, their weights, velocities and intervehicular distances. [ABSTRACT FROM AUTHOR]

To determine the number of clusters in the clustering analysis that has a broad range of applied sciences, such as physics, chemistry, biology, engineering, economics etc., many methods have been proposed in the literature. The aim of this paper is to determine the number of clusters of a dataset in a model-based clustering by using an Analytic Hierarchy Process (AHP). In this study, the AHP model has been created by using the information criteria Akaike's Information Criterion (AIC), Approximate Weight of Evidence (AWE), Bayesian Information Criterion (BIC), Classification Likelihood Criterion (CLC), and Kullback Information Criterion (KIC). The achievement of the proposed approach has been tested on common real and synthetic datasets. The proposed approach based on the corresponding information criteria has produced accurate results. The currently produced results have been seen to be more accurate than those corresponding to the information criteria. [ABSTRACT FROM AUTHOR]

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better. [ABSTRACT FROM AUTHOR]

Aim The objective of this paper is to identify attribute patterns of influential individuals in intensive care units using unsupervised cluster analysis. Background Despite the acknowledgement that culture of an organisation is critical to improving patient safety, specific methods to shift culture have not been explicitly identified. Methods A social network analysis survey was conducted and an unsupervised cluster analysis was used. Results A total of 100 surveys were gathered. Unsupervised cluster analysis was used to group individuals with similar dimensions highlighting three general genres of influencers: well-rounded, knowledge and relational. Conclusions Culture is created locally by individual influencers. Cluster analysis is an effective way to identify common characteristics among members of an intensive care unit team that are noted as highly influential by their peers. To change culture, identifying and then integrating the influencers in intervention development and dissemination may create more sustainable and effective culture change. Additional studies are ongoing to test the effectiveness of utilising these influencers to disseminate patient safety interventions. Implications for nursing management This study offers an approach that can be helpful in both identifying and understanding influential team members and may be an important aspect of developing methods to change organisational culture. [ABSTRACT FROM AUTHOR]

There is always a lack of a cluster validity function and optimization strategy to find out clusters and catch the evolution trend of cluster structures on a categorical data stream. Therefore, this paper presents an optimization model for clustering categorical data streams. In the model, a cluster validity function is proposed as the objective function to evaluate the effectiveness of the clustering model while each new input data subset is flowing. It simultaneously considers the certainty of the clustering model and the continuity with the last clustering model in the clustering process. An iterative optimization algorithm is proposed to solve an optimal solution of the objective function with some constraints. Furthermore, we strictly derive a detection index for drifting concepts from the optimization model. We propose a detection method that integrates the detection index and the optimization model to catch the evolution trend of cluster structures on a categorical data stream. The new method can effectively avoid ignoring the effect of the clustering validity on the detection result. Finally, using the experimental studies on several real data sets, we illustrate the effectiveness of the proposed algorithm in clustering categorical data streams, compared with existing data-streams clustering algorithms. [ABSTRACT FROM AUTHOR]

Clustering real-world data is a challenging task, since many real-data collections are characterized by an inherent sparseness and variable distribution. An appealing domain that generates such data collections is the medical care scenario where collected data include a large cardinality of patient records and a variety of medical treatments usually adopted for a given disease pathology. This paper proposes a two-phase data mining methodology ( M L C ) to iteratively analyze different dataset portions and locally identify groups of objects with common properties. Discovered cohesive clusters are then analyzed using sequential patterns to characterize temporal relationships among data features. To support an automatic classification of new data objects within one of the discovered groups, a classification model is created starting from the computed cluster set. A mobile application has been also designed and developed to visualize and update data under analysis as well as categorizing new unlabeled data objects. The experimental evaluation conducted on real datasets in the medical care scenario showed the effectiveness of M L C to discover interesting knowledge items and to easily exploit them through a mobile application. Results have been also discussed from a medical perspective. [ABSTRACT FROM AUTHOR]

This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using their difference of convex representations. A non-smooth non-convex optimization formulation of the clustering problem is used to design the algorithm. Characterizations of critical points, stationary points in the sense of generalized gradients and inf-stationary points of the clustering problem are given. The proposed algorithm is tested and compared with other clustering algorithms using large real world data sets. [ABSTRACT FROM AUTHOR]

In many data mining tasks, the goal is to classify entities into a set of pre-defined groups (classes). A second and equally important goal is the interpretation, i.e. understanding the nature of the population aggregated in each class. These tasks are rendered even more complex when there is no a-priori information regarding the right classification. The current paper is based on two concepts: (1) Bounded-Rationality theory which implements an S-shaped function that represents human logic as a saliency measure to determine the substantial features that characterize each potential group and (2) Classification by clustering (CBC) that applies Decision Tree-like classification in unsupervised clustering problems, where neither an a-priori classification nor target-attributes are known in advance. In the context of these two concepts, the current research contributes: (1) by expanding the saliency measure to all possible types of variables (nominal as well as numerical), (2) by evaluating, using five datasets, a composite model that combines the CBC method and the saliency concept. The findings show that by using clustering algorithms for classification tasks (CBC method) the results are as accurate as those obtained by conventional Decision Trees, but with a better saliency factor. [ABSTRACT FROM AUTHOR]

Cluster ensemble is one of the main branches in the ensemble learning area which is an important research focus in recent years. The objective of cluster ensemble is to combine multiple clustering solutions in a suitable way to improve the quality of the clustering result. In this paper, we design a new noise immune cluster ensemble framework named as AP^2CE to tackle the challenges raised by noisy datasets. AP^2CE not only takes advantage of the affinity propagation algorithm (AP) and the normalized cut algorithm (Ncut), but also possesses the characteristics of cluster ensemble. Compared with traditional cluster ensemble approaches, AP^2CE is characterized by several properties. ($1$ ) It adopts multiple distance functions instead of a single Euclidean distance function to avoid the noise related to the distance function. ( $2$ ) AP^2CE applies AP to prune noisy attributes and generate a set of new datasets in the subspaces consists of representative attributes obtained by AP. ( $3$ ) It avoids the explicit specification of the number of clusters. ($4$ ) AP^2CE adopts the normalized cut algorithm as the consensus function to partition the consensus matrix and obtain the final result. In order to improve the performance of AP^2CE, the adaptive AP^2CE is designed, which makes use of an adaptive process to optimize a newly designed objective function. The experiments on both synthetic and real datasets show that ($1$ ) AP^2CE works well on most of the datasets, in particular the noisy datasets; ($2$ ) AP^2CE is a better choice for most of the datasets when compared with other cluster ensemble approaches; ( $3$ ) AP^2CE has the capability to provide more accurate, stable and robust results. [ABSTRACT FROM PUBLISHER]

Network public opinion analysis is an important way of information analysis processing. This paper based on the research of the related technologies, designs and realizes a new network public opinion analysis system. System mainly includes network data fetching part, fetching the data processing part, analyzes the processed data part and display part of the public opinion analysis results. In the document extraction part, used the web crawler technology, Larbin web crawler to realize the collection of web content; In public opinion information analysis part, the implementation of the new topic adopts an improved Single - Pass clustering algorithm. This algorithm is using of multi-center, using the title and body of the vector to compared two-way, that is better reflect the dynamics of public opinion topics. Finally, in the network environment of a university, we have the tests repeatedly. The results show that the new public opinion analysis system running is stable and has good efficiency. The thesis has certain value for the development of other information analysis systems in the Internet. [ABSTRACT FROM AUTHOR]

Fuzzy C-means has been utilized successfully in a wide range of applications, extending the clustering capability of the K-means to datasets that are uncertain, vague and otherwise hard to cluster. This paper introduces the Fuzzy C-means++ algorithm which, by utilizing the seeding mechanism of the K-means++ algorithm, improves the effectiveness and speed of Fuzzy C-means. By careful seeding that disperses the initial cluster centers through the data space, the resulting Fuzzy C-means++ approach samples starting cluster representatives during the initialization phase. The cluster representatives are well spread in the input space, resulting in both faster convergence times and higher quality solutions. Implementations in R of standard Fuzzy C-means and Fuzzy C-means++ are evaluated on various data sets. We investigate the cluster quality and iteration count as we vary the spreading factor on a series of synthetic data sets. We run the algorithm on real world data sets and to account for the non-determinism inherent in these algorithms we record multiple runs while choosing different k parameter values. The results show that the proposed method gives significant improvement in convergence times (the number of iterations) of up to 40 (2.1 on average) times the standard on synthetic datasets and, in general, an associated lower cost function value and Xie–Beni value. A proof sketch of the logarithmically bounded expected cost function value is given. [ABSTRACT FROM AUTHOR]

Clustering analysis is a powerful tool in customer segmentation. Although various algorithms have been proposed, the determination of the optimal number of clusters remains to be a difficult issue. In this paper, a clustering method based on consistency criterion is proposed to address this issue. The main characteristic of the new approach is that it requires little prior information and can find the optimal number of clusters automatically. Extensive comparisons are done over 22 real-world datasets from different domains, in which four well-known clustering algorithms in combination with six clustering indices are used as the benchmark methods. The results demonstrate the superiority of our method in appropriately determining the number of clusters. An application of the new approach in customer segmentation of credit card users is also illustrated. [ABSTRACT FROM AUTHOR]

Several clustering analysis techniques for categorical data exist to divide similar objects into groups. Some are able to handle uncertainty in the clustering process, whereas others have stability issues. In this paper, we propose a new technique called TMDP (Total Mean Distribution Precision) for selecting the partitioning attribute based on probabilistic rough set theory. On the basis of this technique, with the concept of granularity, we derive a new clustering algorithm, MTMDP (Maximum Total Mean Distribution Precision), for categorical data. The MTMDP algorithm is a robust clustering algorithm that handles uncertainty in the process of clustering categorical data. We compare the MTMDP algorithm with the MMR (Min–Min–Roughness) algorithm which is the most relevant clustering algorithm, and also compared it with other unstable clustering algorithms, such as k -modes, fuzzy k -modes and fuzzy centroids. The experimental results indicate that the MTMDP algorithm can be successfully used to analyze grouped categorical data because it produces better clustering results. [ABSTRACT FROM AUTHOR]