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• Gas turbine life management monitors and assesses turbine status and conditions. • Inlet and exit air passages impact gas turbine performance, not physical attributes. • Study gap: An incomplete gas-turbine model limits big data's predictive analysis. • Big data analytics drive optimized informed integrated health monitoring decisions for gas turbines. • Data mining extends gas turbine life by forecasting faults for the future. In addition to power generation, gas turbines (GT) are crucial components in many engineering applications, including those for aircraft, ships, and other machinery that needs a prime mover. One of the main concerns was predicting a comprehensive GT model, including intake, exit, and power-generator characteristics. Hence, this study provides a comprehensive review of GT's main components, parameters, faults, and predicting models using machine learning and data mining applications. Forty-four publications between 2014 and 2023 were chosen for this review study based on the primary keywords associated with the paper title. This study focuses solely on the GT model prediction aspects, considering the significance of field distribution, applications of data mining, research goals, benefits, constraints, and contributions to aviation, the oil and gas industry, and marine propulsion fields. Based on the examined studies, ANN, ELM, and KF applications show promise for potential GT life management. The typical limitations of GT life prediction, as observed in 93.2% of the review papers, involved the absence of a comprehensive GT model that incorporates all vibration factors and the use of substantial amounts of real data for predictive modelling. Another notable finding that underscores the knowledge gap in this area is the utilization of complete GT units to monitor unit life and facilitate life management. Apparently, the development of GT life management is still riddled with several limitations, which call for more improvement. [ABSTRACT FROM AUTHOR]

Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new adaptive rule-based classifier for multi-class classification of biological data, where several problems of classifying biological data are addressed: overfitting, noisy instances and class-imbalance data. It is well known that rules are interesting way for representing data in a human interpretable way. The proposed rule-based classifier combines the random subspace and boosting approaches with ensemble of decision trees to construct a set of classification rules without involving global optimisation. The classifier considers random subspace approach to avoid overfitting, boosting approach for classifying noisy instances and ensemble of decision trees to deal with class-imbalance problem. The classifier uses two popular classification techniques: decision tree and k-nearest-neighbor algorithms. Decision trees are used for evolving classification rules from the training data, while k-nearest-neighbor is used for analysing the misclassified instances and removing vagueness between the contradictory rules. It considers a series of k iterations to develop a set of classification rules from the training data and pays more attention to the misclassified instances in the next iteration by giving it a boosting flavour. This paper particularly focuses to come up with an optimal ensemble classifier that will help for improving the prediction accuracy of DNA variant identification and classification task. The performance of proposed classifier is tested with compared to well-approved existing machine learning and data mining algorithms on genomic data (148 Exome data sets) of Brugada syndrome and 10 real benchmark life sciences data sets from the UCI (University of California, Irvine) machine learning repository. The experimental results indicate that the proposed classifier has exemplary classification accuracy on different types of biological data. Overall, the proposed classifier offers good prediction accuracy to new DNA variants classification where noisy and misclassified variants are optimised to increase test performance. [ABSTRACT FROM AUTHOR]

• Two phase MapReduce algorithm is proposed for incremental mining of sequential patterns. • Backward mining makes use of the knowledge obtained during the previous mining process. • Co-occurrence reverse map data structure efficiently generates the candidate sequences. • Candidate generation rules avoids the generation of too many false candidates. • Three novel early prune properties are introduced based on the study of item co-occurrences. Sequential Pattern Mining (SPM) is a popular data mining task with broad applications. With the advent of big data, traditional SPM algorithms are not scalable. Hence, many of the researchers have migrated to big data frameworks such as MapReduce and proposed distributed algorithms. However, the existing MapReduce algorithms assume the data as static and do not handle the incremental database updates. Moreover, they use to re-mine the updated database while new sequences are inserted. In this paper, we propose an efficient distributed algorithm for incremental sequential pattern mining (MR-INCSPM) using the MapReduce framework that can handle big data. The proposed algorithm incorporates the backward mining approach that efficiently makes use of the knowledge obtained during the previous mining process. Also, based on the study of item co-occurrences, we propose Co-occurrence Reverse Map (CRMAP) data structure. The issue of combinatorial explosion of candidate sequences is dealt using the proposed CRMAP data structure. Besides, a novel candidate generation and early prune mechanisms are designed using CRMAP to speed up the mining process. The proposed algorithm is evaluated on both the real and synthetic datasets. The experimental results prove the efficacy of MR-INCSPM with respect to processing time, memory and pruning efficiency. [ABSTRACT FROM AUTHOR]

With the increasing popularity of large scale Knowledge Graph (KG)s, many applications such as semantic analysis, search and question answering need to link entity mentions in texts to entities in KGs. Because of the polysemy problem in natural language, entity disambiguation is thus a key problem in current research. Existing disambiguation methods have considered entity prominence, context similarity and entity-entity relatedness to discriminate ambiguous entities, which are mainly working on document or paragraph level texts containing rich contextual information, and based on lexical matching for computing context similarity. When meeting short texts containing limited contextual information, such as web queries, questions and tweets, those conventional disambiguation methods are not good at handling single entity mention and measuring context similarity. In order to enhance the performance of disambiguation methods based on context similarity with such short texts, we propose SCSNED method for disambiguation based on semantic similarity between contextual words and informative words of entities in KGs. Specially, we exploit the effectiveness of both knowledge-based and corpus-based semantic similarity methods for entity disambiguation with SCSNED. Moreover, we propose a Category2Vec embedding model based on joint learning of word and category embedding, in order to compute word-category similarity for entity disambiguation. We show the effectiveness of these proposed methods with illustrative examples, and evaluate their effectiveness in a comparative experiment for entity disambiguation in real world web queries, questions and tweets. The experimental results have identified the effectiveness of different semantic similarity methods, and demonstrated the improvement of semantic similarity methods in SCSNED and Category2Vec over the conventional context similarity baseline. We further compare the proposed approaches with the state of the art entity disambiguation systems and show the performances of the proposed approaches are among the best performing systems. In addition, one important feature of the proposed approaches using semantic similarity, is the potential application on any existing KGs since they mainly use common features of entity descriptions and categories. Another contribution of the paper is an updated survey on background of entity disambiguation in KGs and semantic similarity methods. [ABSTRACT FROM AUTHOR]

In this paper we present PFDCMSS (Parallel Forward Decay Count–Min Space Saving) which, to the best of our knowledge, is the world first message–passing parallel algorithm for mining time–faded heavy hitters. The algorithm is a parallel version of the recently published FDCMSS (Forward Decay Count–Min Space Saving) sequential algorithm. We formally prove its correctness by showing that the underlying data structure, a sketch augmented with a Space Saving stream summary holding exactly two counters, is mergeable. Whilst mergeability of traditional sketches derives immediately from theory, we show that, instead, merging our augmented sketch is non trivial. Nonetheless, the resulting parallel algorithm is fast and simple to implement. The very large volumes of modern datasets in the context of Big Data present new challenges that current sequential algorithms can not cope with; on the contrary, parallel computing enables near real time processing of very large datasets, which are growing at an unprecedented scale. Our algorithm’s implementation, taking advantage of the MPI (Message Passing Interface) library, is portable, reliable and provides cutting–edge performance. Extensive experimental results confirm that PFDCMSS retains the extreme accuracy and error bound provided by FDCMSS whilst providing excellent parallel scalability. Our contributions are three-fold: (i) we prove the non trivial mergeability of the augmented sketch used in the FDCMSS algorithm; (ii) we derive PFDCMSS, a novel message–passing parallel algorithm; (iii) we experimentally prove that PFDCMSS is extremely accurate and scalable, allowing near real time processing of large datasets. The result supports both casual users and seasoned, professional scientists working on expert and intelligent systems. [ABSTRACT FROM AUTHOR]

This paper explores advances in the data mining field to solve the fundamental Document Information Retrieval problem. In the proposed approach, useful knowledge is first discovered by using data mining techniques, then swarms use this knowledge to explore the whole space of documents intelligently. We have investigated two data mining techniques in the preprocessing step. The first one aims to split the collection of documents into similar clusters by using the K-means algorithm, while the second one extracts the most closed frequent terms on each cluster already created using the DCI_Closed algorithm. For the solving step, BSO (Bees Swarm Optimization) is used to explore the cluster of documents deeply. The proposed approach has been evaluated on well-known collections such as CACM (Collection of ACM), TREC (Text REtrieval Conference), Webdocs, and Wikilinks, and it has been compared to state-of-the-art data mining, bio-inspired and other documents information retrieval based approaches. The results show that the proposed approach improves the quality of returned documents considerably, with a competitive computational time compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The arrival of new technologies related to smart grids and the resulting ecosystem of applications and management systems pose many new problems. The databases of the traditional grid and the various initiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary to update these systems for the new smart grid reality. Additionally, it is necessary to take advantage of the information smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework is applied to model the integrated information. [ABSTRACT FROM AUTHOR]

In financial markets, it is both important and challenging to forecast the daily direction of the stock market return. Among the few studies that focus on predicting daily stock market returns, the data mining procedures utilized are either incomplete or inefficient, especially when a large amount of features are involved. This paper presents a complete and efficient data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economic features. Three mature dimensionality reduction techniques, including principal component analysis ( PCA ), fuzzy robust principal component analysis ( FRPCA ), and kernel-based principal component analysis ( KPCA ) are applied to the whole data set to simplify and rearrange the original data structure. Corresponding to different levels of the dimensionality reduction, twelve new data sets are generated from the entire cleaned data using each of the three different dimensionality reduction methods. Artificial neural networks ( ANN s) are then used with the thirty-six transformed data sets for classification to forecast the daily direction of future market returns. Moreover, the three different dimensionality reduction methods are compared with respect to the natural data set. A group of hypothesis tests are then performed over the classification and simulation results to show that combining the ANN s with the PCA gives slightly higher classification accuracy than the other two combinations, and that the trading strategies guided by the comprehensive classification mining procedures based on PCA and ANN s gain significantly higher risk-adjusted profits than the comparison benchmarks, while also being slightly higher than those strategies guided by the forecasts based on the FRPCA and KPCA models. [ABSTRACT FROM AUTHOR]

As a new data representation in the big data era, a multi-scale decision information system (MSDIS) realizes "multi-level, multi-angle and multi-view" evaluations in various problems. However, most of existing multi-scale data analysis models are built for complete information systems (CISs), whereas the research on incomplete multi-scale decision information systems (I-MSDISs) is not perfect. Additionally, the irrational behavior of one decision-maker (DM) often has an impact on decision outcomes. On this basis, this paper develops a prospect-regret theory-based three-way decision (3WD) model with intuitionistic fuzzy numbers (IFNs) under an I-MSDIS, which is abbreviated as PR-3WD-I-MSDIS. Specifically, we first select the optimal scale combination of I-MSDISs by the matrix theory to complete the information extraction of incomplete optimal sub-systems. Then, we further propose an attribute weight evaluation strategy under the incomplete optimal subsystem, which fully considers the neighborhood information of each object. Subsequently, the concept and calculation method of aggregated weighted fuzzy conditional probabilities are constructed from the perspective of intuitionistic fuzzy sets. Furthermore, a prospect-regret theory-based relative profit function is proposed, which sufficiently takes into account the loss and utility in decision-making processes. Finally, a novel three-way classification and ranking method is developed for solving incomplete multi-scale problems. The experimental results on real-world datasets demonstrate that the PR-3WD-I-MSDIS model achieves excellent performance. • A prospect-regret theory-based 3WD in an I-MSDIS is designed. • An aggregated weighted fuzzy conditional probability is proposed. • Relative profit function of each object is defined by comprehensively two perspectives. • The selection of the optimal scale combination by matrix theory is explored. [ABSTRACT FROM AUTHOR]

Cluster analysis, or clustering, refers to the analysis of the structural organization of a data set. This analysis is performed by grouping together objects of the data that are more similar among themselves than to objects of different groups. The sampled data may be described by numerical features or by a symbolic representation, known as categorical features. These features often require a transformation into numerical data in order to be properly handled by clustering algorithms. The transformation usually assigns a weight for each feature calculated by a measure of importance (i.e., frequency, mutual information). A problem with the weight assignment is that the values are calculated with respect to the whole set of objects and features. This may pose as a problem when a subset of the features have a higher degree of importance to a subset of objects but a lower degree with another subset. One way to deal with such problem is to measure the importance of each subset of features only with respect to a subset of objects. This is known as co-clustering that, similarly to clustering, is the task of finding a subset of objects and features that presents a higher similarity among themselves than to other subsets of objects and features. As one might notice, this task has a higher complexity than the traditional clustering and, if not properly dealt with, may present an scalability issue. In this paper we propose a novel co-clustering technique, called HBLCoClust, with the objective of extracting a set of co-clusters from a categorical data set, without the guarantees of an enumerative algorithm, but with the compromise of scalability. This is done by using a probabilistic clustering algorithm, named Locality Sensitive Hashing, together with the enumerative algorithm named InClose. The experimental results are competitive when applied to labeled categorical data sets and text corpora. Additionally, it is shown that the extracted co-clusters can be of practical use to expert systems such as Recommender Systems and Topic Extraction. [ABSTRACT FROM AUTHOR]

• Two algorithms that determine different initial cluster centers are proposed. • It is aimed to perform clustering by considering existing fixed cluster centers. • The proposed algorithms are applied to real-life spatial data having fixed centers. • FC-Kmeans converged to K-means performance at fixed center ratio of 43% and below. Clustering is one of the data mining methods that partition large-sized data into subgroups according to their similarities. K-means clustering algorithm works well in spherical or convex data distribution of large-sized data sets. Most of the algorithms based on K-means have generally been interested in an initial cluster centers selection or cluster distribution. However, these algorithms may not meet satisfy some requirements in practice. This paper presents the FC-Kmeans algorithm, which enables clustering by fixing some cluster centers considering real conditions. Thus, while some of the cluster centers are fixed, it is tried to obtain the most appropriate cluster centers for the others and the best distribution of the data to the clusters. The K-means clustering algorithm is compared with two different fixed-centered clustering algorithms which are FC-Kmeans and FC-Kmeans 2. The experimental results show that although the FC-Kmeans algorithm has more limitations than K-means, it converges the performance of K-means algorithm according to some performance indicators such as SSE, DB Index and Silhouette Index. [ABSTRACT FROM AUTHOR]

Social networks have taken an irreplaceable role in our lives. They are used daily by millions of people to communicate and inform themselves. This success has also led to a lot of irrelevant content and even misinformation on social media. In this paper, we propose a user-centred framework to reduce the amount of irrelevant content in social networks to support further stages of data mining processes. The system also helps in the reduction of misinformation in social networks, since it selects credible and reputable users. The system is based on the belief that if a user is credible then their content will be credible. Our proposal uses word embeddings in a first stage, to create a set of interesting users according to their expertise. After that, in a later stage, it employs social network metrics to further narrow down the relevant users according to their credibility in the network. To validate the framework, it has been tested with two real Big Data problems on Twitter. One related to COVID-19 tweets and the other to last United States elections on 3rd November. Both are problems in which finding relevant content may be difficult due to the large amount of data published during the last years. The proposed framework, called NOFACE, reduces the number of irrelevant users posting about the topic, taking only those that have a higher credibility, and thus giving interesting information about the selected topic. This entails a reduction of irrelevant information, mitigating therefore the presence of misinformation on a posterior data mining method application, improving the obtained results, as it is illustrated in the mentioned two topics using clustering, association rules and LDA techniques. • A new framework for filtering irrelevant content in Twitter has been proposed. • First time that word embeddings and bios are used to obtain user expertise. • The framework filters content of social networks keeping only those relevant to the topic intend to study. • The proper performance has been tested on two real problems. [ABSTRACT FROM AUTHOR]

With the advent of Big Data, data is being collected at an unprecedented fast pace, and it needs to be processed in a short time. To deal with data streams that flow continuously, classical batch learning algorithms cannot be applied and it is necessary to employ online approaches. Online learning consists of continuously revising and refining a model by incorporating new data as they arrive, and it allows important problems such as concept drift or management of extremely high-dimensional datasets to be solved. In this paper, we present a unified pipeline for online learning which covers online discretization, feature selection and classification. Three classical methods—the k -means discretizer, the χ 2 filter and a one-layer artificial neural network—have been reimplemented to be able to tackle online data, showing promising results on both synthetic and real datasets. [ABSTRACT FROM AUTHOR]

The recent developments in cellular networks, along with the increase in services, users and the demand of high quality have raised the Operational Expenditure (OPEX). Self-Organizing Networks (SON) are the solution to reduce these costs. Within SON, self-healing is the functionality that aims to automatically solve problems in the radio access network, at the same time reducing the downtime and the impact on the user experience. Self-healing comprises four main functions: fault detection, root cause analysis, fault compensation and recovery. To perform the root cause analysis (also known as diagnosis), Knowledge-Based Systems (KBS) are commonly used, such as fuzzy logic. In this paper, a novel method for extracting the Knowledge Base for a KBS from solved troubleshooting cases is proposed. This method is based on data mining techniques as opposed to the manual techniques currently used. The data mining problem of extracting knowledge out of LTE troubleshooting information can be considered a Big Data problem. Therefore, the proposed method has been designed so it can be easily scaled up to process a large volume of data with relatively low resources, as opposed to other existing algorithms. Tests show the feasibility and good results obtained by the diagnosis system created by the proposed methodology in LTE networks. [ABSTRACT FROM AUTHOR]

• Proposing a fast and accurate version of DBSCAN algorithm for big data. • Calculating density based on a small subset of data called operational set. • Creating and updating the operational set at a very low computational cost. • Comprehensive evaluation of the proposed method in compared to other recent works. Today, data is being generated with a high speed. Managing large volume of data has become a challenge in the current age. Clustering is a method to analyze data that is generated in the Internet. Various approaches have been presented for data clustering until now. Among them, DBSCAN is a most well-known density-based clustering algorithm. This algorithm can detect clusters of different shapes and does not require prior knowledge about the number of clusters. A major part of the DBSCAN run-time is spent to calculate the distance of data from each other to find the neighbors of each sample in the dataset. The time complexity of this algorithm is O(n2); Therefore, it is not suitable for processing big datasets. In this paper, DBSCAN is improved so that it can be applied to big datasets. The proposed method calculates accurately each sample density based on a reduced set of data. This reduced set is called the operational set. This collection is updated periodically. The use of local samples to calculate the density has greatly reduced the computational cost of clustering. The empirical results on various datasets of different sizes and dimensions show that the proposed algorithm increases the clustering speed compared to recent related works while having similar accuracy as the original DBSCAN algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of mining structured data to find potentially useful patterns by association rule mining. Different than the traditional find-all-then-prune approach, a heuristic method is proposed to extract mostly associated patterns (MASPs). This approach utilizes a maximally-association constraint to generate patterns without searching the entire lattice of item combinations. This approach does not require a pruning process. The proposed approach requires less computational resources in terms of time and memory requirements while generating a long sequence of patterns that have the highest co-occurrence. Furthermore, k -item patterns can be obtained thanks to the sub-lattice property of the MASPs. In addition, the algorithm produces a tree of the detected patterns; this tree can assist decision makers for visual analysis of data. The outcome of the algorithm implemented is illustrated using traffic accident data. The proposed approach has a potential to be utilized in big data analytics. [ABSTRACT FROM AUTHOR]

The worldwide growth of e-commerce has created new challenges for logistics companies, such as delivering products quickly and cheaply. This paper presents a heuristic to solve the last-mile route creation problem dynamically. The heuristic is based on a multi-agent system integrated with trajectory data mining techniques to extract territorial patterns and use them to solve the Dynamic Capacitated Vehicle Routing Problem with Stochastic Customers. Our solution approach is focused on a linear-time heuristic that depends only on the Warehouse system configurations and not on the total number of packages processed, which is suitable for express delivery logistics companies that must process a large number of packages per day. We compare our proposal with benchmark algorithms from the literature; additionally, we evaluate its performance and robustness under different scenarios. Results show that our solution approach is effective for scenarios in which routes must be set dynamically from a continuous stream of packages. • Trajectory data mining can improve last-mile routes. • Stochastic and dynamic VRP solutions make cross-docking operations more efficient. • Multi-agent systems allow efficiently implementing the dynamic VRP in warehouse systems. [ABSTRACT FROM AUTHOR]

• A cellular-based evolutionary fuzzy system for the extraction of emerging patterns in high-speed, massive data streams is proposed. • Smart triggering of the learning method which updates the model only when required. • A reinitialisation and filtering strategy for reducing the extraction of redundant patterns is also defined. • The quality of knowledge extracted outperforms state-of-the-art methods. • The proposed method is able to process batches of data up to 750,000 instances. Today, the number of existing devices generates immense amounts of data on a continuous basis that must be processed by new distributed data stream mining approaches. In this paper we present a new approach for extracting descriptive emerging patterns in massive data streams from different sources through Apache Kafka and Apache Spark Streaming whose objective is to monitor the state of the system with respect to a variable of interest. For this purpose, the proposed algorithm is a cellular-based multi-objective evolutionary fuzzy system that uses an informed strategy for efficient data processing and a re-initialisation and filtering mechanism to eliminate redundant and low-reliable patterns. The experimental study carried out demonstrates an interpretability improvement of 25% in the extraction of high-interest knowledge by the proposed algorithm, which would make it easier for experts to analyse the problem. Finally, the proposed algorithm is up to five times faster than another proposal on the processing of the same amount of data. In this experimental study, up to 750,000 instances have been processed in approximately four seconds. [ABSTRACT FROM AUTHOR]

• Some ELMs are used in parallel as experts to map data into discriminative space. • The training data are easily grouped into some clusters in the new feature space. • An index is calculated per group to help finding corresponding groups of a sample. • A tree is used to find corresponding groups of a sample and applying kNN on it. • The method is evaluated on 4 big datasets in absence and presence of noise. kNN algorithm, as an effective data mining technique, is always attended for supervised classification. On the other hand, the previously proposed kNN finding methods cannot be considered as efficient methods for dealing with big data. As there is daily generated and expanded big datasets on different online and offline servers, the efficient methods for such data must be introduced to find kNN. Moreover, massive amounts of data contain more noise and imperfection data samples that significantly increase the need for a robust kNN finding method. In this paper, a new fast and robust kNN finding framework is introduced to deal with the big datasets. In this method, a group of most relevant data samples to an input data sample are detected and the original kNN method is applied on them for finding the final nearest neighbors. The main goal of this method is dealing with the big datasets in an accurate, fast, and robust manner. Here, the training data samples of each label are grouped into some partitions based on the output of some mini-classifiers (i.e. ELM classifier). In fact, the behavior of the mini-classifiers is the basis of partitioning the training data samples. These mini-classifiers are trained using non-overlapping subsets of the training set in the form of each mini-classifier a subset. Here, an index is calculated for each partition to make the corresponding partition finding faster using a tree structure in which each partition index is fallen into a leaf. Then, outputs of the mini-classifiers for an input test sample are used to find the corresponding group of most relevant data samples to the input data sample on the tree. Experimental results indicate that the proposed method has better performance in most cases and comparable performance on other cases of original and noisy big data problems. [ABSTRACT FROM AUTHOR]

• Detecting outliers is vital in data mining and classification algorithms. • In this sense, we propose an evolutionary Pentagon Support Vector finder method. • We use geometrical calculations and evolutionary clustering to make a more effective system. • Our proposed approach successfully removes outliers from all data sets. • We do not lose vital samples and do not harm final accuracy. In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary Pentagon Support Vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy on some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results. [ABSTRACT FROM AUTHOR]

• DP3, a high-performance distributed parallel algorithm for Frequent Itemsets Mining. • FPO tree for optimal compactness for light transfers and efficient aggregations. • Shared-memory parallel to exhaust multi-core CPUs resource of distributed nodes. • Obtain memory scalability, load balance for shared-memory and distributed parallels. • DP3 far outperforms well-known and recently high-performance algorithms. Frequent Itemsets Mining is a fundamental mining model in Data Mining. It supports a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Many distributed parallel algorithms have been introduced to confront with very large-scale datasets of Big Data. However, the problems of running time and memory scalability still have not had adequate solutions for very large and "hard-to-mined" datasets. In this paper, we propose a distributed parallel algorithm named DP3 (D istributed P re P ost P lus) which parallelizes the state-of-the-art algorithm PrePost+ and operates in Master-Slaves model. Slave machines mine and send local frequent itemsets and support counts to the Master for aggregations. In the case of tremendous numbers of itemsets transferred between the Slaves and Master, the computational load at the Master, therefore, is extremely heavy if there is not the support from our complete FPO tree (F requent P atterns O rganization) which can provide optimal compactness for light data transfers and highly efficient aggregations with pruning ability. Processing phases of the Slaves and Master are designed for memory scalability and shared-memory parallel in Work-Pool model so as to utilize the computational power of multi-core CPUs. We conducted experiments on both synthetic and real datasets, and the empirical results have shown that our algorithm far outperforms the well-known PFP and other three recently high-performance ones Dist-Eclat, BigFIM, and MapFIM. [ABSTRACT FROM AUTHOR]