Data Partitioning Algorithm Research Articles

Cluster analysis applied to obtaining reference models for building thermal performance studies

Cluster analysis has often been used to obtain reference models. However, using different methods in each analysis step may yield different results. Furthermore, the validation of the results is often neglected. This study aims to develop a cluster analysis method to obtain reference models for use in thermal performance studies of buildings from different clustering configurations. The procedure proposed is composed of six steps: (1) initial database composition, (2) data matrices formation, (3) cluster analysis, (4) determination of reference models, (5) methods validation and (6) results interpretation. Different data treatments, similarity measures and partitioning algorithms were combined in the cluster analysis to define the method with the best cluster formation. The weighting factor was the data treatment that most contributed to obtaining suitable clustering solutions. City-block, Euclidean Distance and Squared Euclidean Distance similarity measures resulted in suitable formations as well as Complete Linkage, Ward and K-means algorithms. Two clusters were obtained from the cluster methodology, for which two reference models were determined. Hypothesis tests showed that clusters differ for most performance indicators. It was concluded that the procedure proposed could identify the combination of methods that results in the best application of cluster analysis. The main contribution of this paper is to introduce a procedure for validating clusters. This procedure is an applicable technique to obtain reference models.

Autonomous anomaly detection for streaming data

Anomaly detection from data streams is a hotly studied topic in the machine learning domain. It is widely considered a challenging task because the underlying patterns exhibited by the streaming data may dynamically change at any time. In this paper, a new algorithm is proposed to detect anomalies autonomously for streaming data. The proposed algorithm is nonparametric and does not require any threshold to be preset by users. The algorithmic procedure of the proposed algorithm is composed of the following three complementary stages. Firstly, the potentially anomalous samples that represent highly different patterns from others are identified from data streams based on data density. Then, these potentially anomalous samples are clustered online using the evolving autonomous data partitioning algorithm. Finally, true anomalies are identified from these minor clusters with the least amounts of samples associated with them. Numerical examples based on three benchmark datasets demonstrated the potential of the proposed algorithm as a highly effective approach for anomaly detection from data streams.

Development of a hybrid VRF system energy consumption prediction model based on data partitioning and swarm intelligence algorithm

Accurately forecasting energy consumption is beneficial and pivotal for effectively managing variable refrigerant flow (VRF) systems. Changes in energy consumption provide an intuitive representation of the operating condition and the impact of possible faults. Energy consumption prediction is fundamental in energy conservation tasks such as fault diagnosis. Currently, many energy consumption prediction model have been applied for HVAC system widely. However black-box energy prediction models applied to VRF systems are still relatively few and crude. This study therefore proposed a hybrid energy consumption prediction method for VRF systems based on data partitioning and swarm intelligence algorithm. The model was based on back propagation neural network (BPNN), and utilized data partitioning techniques to identify the energy consumption patterns of the VRF system. For each pattern, a BPNN sub-model was trained using operating and energy consumption data of a VRF system. Then, swarm intelligence algorithm is adopted to determine the optimal architecture of each BPNN sub-model. The results demonstrate that the proposed hybrid models can achieve better prediction performance than single models like BP Neural Network. Root mean square error and mean absolute percentage error of the proposed predictive model is 202.49 and 1.95%, which has a 31.3% and 45.5% decrease compared to single BPNN model. At the same time, it is enlightening for other researchers to study the potential of data partitioning algorithm and swarm intelligence algorithm in the field of VRF system energy consumption prediction.

An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

Accurately clustering large, high dimensional datasets is a challenging problem in unsupervised learning. K-means is considered to be a fast, widely used and accurate centroid based data partitioning algorithm for spherical datasets. However, its non-determinism and heavy dependence on the selection of initial cluster centers along with vulnerability to noise make it a poor candidate for clustering large datasets with high dimensionality. To overcome these, we develop a novel, nature inspired, centroid based clustering algorithm, inspired from the principles of particle physics. Our method ensures that the convergence to local optima and non-deterministic outputs are avoided. We experiment the method on large datasets of human face images. Besides, our method addresses the problem of outliers and presence of not well-separated data in these datasets. We use a deep learning model for extracting facial features into a vector of 128 dimensions. We validate the quality and accuracy of our methods using different statistical parameters like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f-measure, accuracy, error rate, average in group proportion and normalized cluster size rand index</i> . These evaluations show that our method exhibits better accuracy and quality in clustering large face image datasets, in comparison with other existing mechanisms. The strength of our algorithms is more visible as the size of the dataset grows.

Cloud-native distributed genomic pileup operations.

Pileup analysis is a building block of many bioinformatics pipelines, including variant calling and genotyping. This step tends to become a bottleneck of the entire assay since the straightforward pileup implementations involve processing of all base calls from all alignments sequentially. On the other hand, a distributed version of the algorithm faces the intrinsic challenge of splitting reads-oriented file formats into self-contained partitions to avoid costly data exchange between computational nodes. Here, we present a scalable, distributed, and efficient implementation of a pileup algorithm that is suitable for deploying in cloud computing environments. In particular, we implemented: (i) our custom data-partitioning algorithm optimized to work with the alignment reads, (ii) a novel and unique approach to process alignment events from sequencing reads using the MD tags, (iii) the source code micro-optimizations for recurrent operations, and (iv) a modular structure of the algorithm. We have proven that our novel approach consistently and significantly outperforms other state-of-the-art distributed tools in terms of execution time (up to 6.5x faster) and memory usage (up to 2x less), resulting in a substantial cloud cost reduction. SeQuiLa is a cloud-native solution that can be easily deployed using any managed Kubernetes and Hadoop services available in public clouds, like Microsoft Azure Cloud, Google Cloud Platform or Amazon Web Services. Together with the already implemented distributed range joins and coverage calculations, our package provides end-users with an unified SQL interface for convenient analyzing of population-scale genomic data in an interactive way. https://biodatageeks.github.io/sequila/. Supplementary data are available at Bioinformatics online.

PROADAPT: Proactive framework for adaptive partitioning for big data warehouses

Parallel DBMSs have become more and more mature and getting several success stories in the industry. This situation has been reached by powerful data partitioning and data allocation techniques and algorithms. By analyzing these findings closely, we figure out that they are quickly stressed by the workload changes, which represents the usual case of business analytics applications. To deal with this challenge in the context of big data warehouses, several studies proposed to move to another processing paradigm outside the DBMS realm such as Spark by proposing adaptive partitioning solutions to tackle the workload changes. The majority of approaches are offline and those that are online cause significant random disk I/O costs. This is because the correlation that may exist between jobs and data blocks read from the disk is not captured to refine the adaptive partitioning algorithms. This represents one of the major causes of providing high performance of dynamic workloads. To solve such limitations, we propose in this paper a proactive framework for query-aware adaptive partitioning (called PROADAPT) that uses an AI-inspired methodology that can be connected to any query optimizer managing partitioned data. This methodology uses a genetic algorithm to solve our formalized problem that considers the interaction that may exist among workload queries. PROADAPT intensively rewrites queries by exploiting dimension hierarchies to skip irrelevant data and then improves I/O performance. Different technical modules of our framework are discussed. Finally, we conduct intensive experiments on Postgres-XL and a Spark SQL parallel cluster to show the effectiveness and efficiency of our approach.

Materialisation and data partitioning algorithms for distributed RDF systems

Many RDF systems support reasoning with Datalog rules via materialisation, where all conclusions of RDF data and the rules are precomputed and explicitly stored in a preprocessing step. As the amount of RDF data used in applications keeps increasing, processing large datasets often requires distributing the data in a cluster of shared-nothing servers. While numerous distributed query answering techniques are known, distributed materialisation is less well understood. In this paper, we present several techniques that facilitate scalable materialisation in distributed RDF systems. First, we present a new distributed materialisation algorithm that aims to minimise communication and synchronisation in the cluster. Second, we present two new algorithms for partitioning RDF data, both of which aim to produce tightly connected partitions, but without loading complete datasets into memory. We evaluate our materialisation algorithm against two state-of-the-art distributed Datalog systems and show that our technique offers competitive performance, particularly when the rules are complex. Moreover, we analyse in depth the effects of data partitioning on reasoning performance and show that our techniques offer performance comparable or superior to the state of the art min-cut partitioning, but computing the partitions requires considerably less time and memory.

EFNN-NullUni: An evolving fuzzy neural network based on null-uninorm

Interpretability in intelligent models becomes a challenge in academic research and approaches that facilitate understanding the responses obtained in models based on artificial intelligence and machine learning. This paper presents a new logical fuzzy neuron based on the concept of null-uninorm, thus called null-unineuron to compose the architecture of an evolving neuro-fuzzy model. This new structural neuron can extract advanced fuzzy rules allowing AND and OR-connections of antecedents to better interpret and understand the analyzed problem. This three-layer model uses an evolving weighted fuzzification approach based on incremental data partitioning concepts for knowledge extraction through null-unineurons whose training procedure suits the classification of binary and multiclass patterns in an online and incremental way. The weights integrated in the evolving data partitioning algorithm belong to feature importance levels and achieve an automatic shrinkage of distance calculations along unimportant input directions (features), which in turn accounts for a soft dimension reduction and the likelihood to decrease over-fitting. The new architecture proposed in this model was, subject to pattern classification tests, being more efficient compared to related (evolving) neuro-fuzzy models in the literature. Finally, experiments on various real-world data sets proved that the evolving neuro-fuzzy model proposed in this paper can act in a simplified way in the extraction of knowledge from data while providing answers with a high degree of accuracy for pattern classification problems.

Trapezoidal type-2 fuzzy inference system with tensor unfolding structure learning method

In the paper, trapezoidal type-2 fuzzy inference system (TT2FIS) is introduced. To construct the type-2 fuzzy inference system, trapezoidal type-2 fuzzy sets are adopted to construct the antecedent part of the fuzzy rules, the centers of the fuzzy sets are roughly clustered by an evolving autonomous data partitioning algorithm. To further eliminate the effect of the centers that are generated by dataset itself, and it can be easily impacted by the unbalance data, the data clouds that are generated by autonomous data partitioning algorithm are filtered by Hammersley sequence, and symmetrical trapezoidal type-2 fuzzy sets are generated by the generated cluster centers and problem-free standard deviation generating method. To the consequent part, generalized type-2 TSK consequent is formulated by tensor, which is obtained via three constituent parts of the trapezoidal type-2 fuzzy sets (lower membership function, upper membership function and type-reduction set of data samples), the parameters of consequent part are the iterative results of a matrix equation that is unfolded from the tensor. Finally, simulation results are carried out to verify the effectiveness of the proposed trapezoidal type-2 fuzzy inference system. Simulation results show that the generalization of the TT2FIS is better than the coincide type-2 fuzzy inference system or adaptive type-1 fuzzy inference system.

Application of NIR spectroscopy and multivariate analysis for Non-destructive evaluation of apple moisture content during ultrasonic drying

Direct-contact ultrasonic drying is a novel approach to dehydrate fruits and vegetables to reduce microbial growth and post-harvest loss while preserving nutrients and the quality of the final product. Moisture content is a critical component for food behavior during drying, and its accurate evaluation in real-time is essential for food quality control. This study conveys the potential implementation of portable near-infrared spectroscopy (NIRS) combined with multivariate analysis for real-time assessment of moisture content in apple slices during direct-contact ultrasonic drying. Partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed, and their performances for different pre-treatments methods and data partitioning algorithms were evaluated with both internal cross-validation and an external dataset. Three wavelengths were selected by SPA (1359, 1517, and 1594 nm) which were then used to introduce a closed-form equation for moisture content prediction with R2p = 0.99 and RMSEP = 3.32%. The results revealed that portable NIRS combined with multivariate analysis is quite promising for monitoring and evaluating the moisture content during ultrasonic drying.

Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. A K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

Clustering-based rule generation methods for fuzzy classifier using Autonomous Data Partitioning algorithm

In this paper, clustering-based rule generation methods for fuzzy classifier using non-parametric Autonomous Data Partitioning algorithm have been proposed. ADP-algorithm is used to determine the number of clusters for use in various k-means-like clustering algorithms. Proposed method contributes to solving the problem of determining optimal number of clusters/rules. The efficiency of fuzzy classifiers with rules constructed by the specified algorithms has been tested on data sets from the KEEL repository. Experimental results show that proposed method outperforms baseline algorithm (the extremums rulebase generation algorithm) both in terms of classification accuracy and geometric mean metrics.

A new model to prevent failures in gas turbine engines based on TSFRESH, self-organized direction aware data partitioning algorithm and machine learning techniques

The gas turbine is of great significance to industries such as aviation, power generation, oil, and gas. Therefore, fault occurrence in this turbomachinery item can lead to operational interruptions, accidents and, hence, to potential economic losses. Regarding these consequences, fault prevention can result in cost reduction, along with productivity and safety increase. This paper aims to discuss an autonomous model based on self-organized direction aware data partitioning algorithm, and machine learning techniques, including features extraction and selection based on hypothesis tests, to prevent failures in gas turbine. The model proposed in this work was tested in a database emulated by ProDiMES (strategy of diagnostic propulsion methods evaluation), which is a benchmarking tool developed by NASA. Additionally, the proposed model can identify the patterns that distinguish the aircraft engine gas path operations as healthy, faulty, or in imminent fault, achieving satisfactory performances in all cases and allowing to define a threshold for anticipating faulty operations.

A novel data partitioning algorithm for dynamic energy optimization on heterogeneous high‐performance computing platforms

SummaryEnergy is one of the most important objectives for optimization on modern heterogeneous high‐performance computing (HPC) platforms. The tight integration of multicore CPUs with accelerators such as graphical processing units (GPUs) and Xeon Phi coprocessors in these platforms presents several challenges to the optimization of multithreaded data‐parallel applications for energy. In this work, the problem of optimization of data‐parallel applications on heterogeneous HPC platforms for dynamic energy through workload distribution is formulated. We propose a workload partitioning algorithm to solve this problem. It employs load‐imbalancing technique to determine the workload distribution minimizing the dynamic energy consumption of the parallel execution of an application. The inputs to the algorithm are discrete dynamic energy profiles of individual computing devices. The profiles are practically constructed using an approach that accurately models the energy consumption by execution of a hybrid scientific data‐parallel application on a heterogeneous platform containing different computing devices such as CPU, GPU, and Xeon Phi. The proposed algorithm is experimentally analyzed using two multithreaded data‐parallel applications, matrix multiplication and 2D fast Fourier transform. The load‐imbalanced solutions provided by the algorithm achieve significant dynamic energy reductions for the two applications (in average by 130% and 44%, respectively) compared with the load‐balanced solutions.

Evolving fuzzy neural hydrocarbon networks: A model based on organic compounds

This paper presents a new evolving intelligent model capable of combining the techniques and concepts of artificial neural networks, fuzzy systems and artificial hydrocarbon networks, in which the latter aggregates concepts of organic chemistry to carry out the training of intelligent models. The proposed model has three layers where the first two form a fuzzy inference system and the third layer is responsible for the defuzzification process through concepts based on the bond between carbons and hydrogens. The fuzzification process of the model is based on the techniques of an autonomous data partitioning algorithm that can elicit the number and centers of the clouds that make up the fuzzy neurons in the first layer of the model. Thereby, an evolving algorithm is employed, which uses the data set as a stream in a single-pass incremental mode (allowing fast processing). This is achieved in an unsupervised manner, and thus, to eliminate possible overfitting problems in the subsequent supervised training process, a Bayesian pruning technique is used to identify the neurons that are finally most relevant to the actual supervised approximation and/or classification problem. To validate the proposed approach, binary pattern classification tests, multi-class classification problems and regression problems were performed. The results obtained and compared with other intelligent models in the literature prove that the approach becomes a model capable of extracting knowledge from data sets and using concepts of organic chemistry to perform learning tasks with high degree of reliability.

Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error

BackgroundRetrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the desire to learn all we can from exhaustive data measurements on trial participants motivates the inclusion of such analyses within RCTs. Moreover, widespread advances in machine learning (ML) methods hold potential to utilise such data to identify subjects exhibiting heterogeneous treatment response.MethodsWe present a novel analysis strategy for detecting TEH in randomised data using ML methods, whilst ensuring proper control of the false positive discovery rate. Our approach uses random data partitioning with statistical or ML-based prediction on held-out data. This method can test for both crossover TEH (switch in optimal treatment) and non-crossover TEH (systematic variation in benefit across patients). The former is done via a two-sample hypothesis test measuring overall predictive performance. The latter is done via ‘stacking’ the ML predictors alongside a classical statistical model to formally test the added benefit of the ML algorithm. An adaptation of recent statistical theory allows for the construction of a valid aggregate p value. This testing strategy is independent of the choice of ML method.ResultsWe demonstrate our approach with a re-analysis of the SEAQUAMAT trial, which compared quinine to artesunate for the treatment of severe malaria in Asian adults. We find no evidence for any subgroup who would benefit from a change in treatment from the current standard of care, artesunate, but strong evidence for significant TEH within the artesunate treatment group. In particular, we find that artesunate provides a differential benefit to patients with high numbers of circulating ring stage parasites.ConclusionsML analysis plans using computational notebooks (documents linked to a programming language that capture the model parameter settings, data processing choices, and evaluation criteria) along with version control can improve the robustness and transparency of RCT exploratory analyses. A data-partitioning algorithm allows researchers to apply the latest ML techniques safe in the knowledge that any declared associations are statistically significant at a user-defined level.

A Hierarchical Data-Partitioning Algorithm for Performance Optimization of Data-Parallel Applications on Heterogeneous Multi-Accelerator NUMA Nodes

Modern HPC platforms are highly heterogeneous with tight integration of multicore CPUs and accelerators (such as Graphics Processing Units, Intel Xeon Phis, or Field-Programmable Gate Arrays) empowering them to address the twin critical concerns of performance and energy efficiency. Due to this inherent characteristic, processing elements contend for shared on-chip resources such as Last Level Cache (LLC), interconnect, etc. and shared nodal resources such as DRAM, PCI-E links, etc., resulting in complexities such as resource contention, non-uniform memory access (NUMA), and accelerator-specific limitations such as limited main memory thereby necessitating support for efficient out-of-card execution. Due to these complexities, the performance profiles of data-parallel applications executing on these platforms are not smooth and deviate significantly from the shapes that allowed state-of-the-art load-balancing algorithms to find optimal solutions. In this paper, we propose a hierarchical two-level data partitioning algorithm minimizing the parallel execution time of data-parallel applications on clusters of $h$ identical nodes where each node has $c$ heterogeneous processors. This algorithm takes as input $c$ discrete speed functions of cardinality $m$ corresponding to the $c$ heterogeneous processors. It does not make any assumptions about the shapes of these functions. Unlike load balancing algorithms, optimal solutions found by the algorithm may not load-balance an application in terms of execution time. The proposed algorithm has low time complexity of $O(m^{2} \times h + m^{3} \times c^{3})$ unlike the state-of-the-art algorithm solving the same problem with the complexity of $O(m^{3} \times c^{3} \times h^{3})$ . We also propose an extension of the algorithm for clusters of $h$ non-identical nodes where each node has $c$ heterogeneous processors. We experimentally demonstrate the optimality of our algorithm using two well-known and highly optimized multi-threaded data-parallel applications, matrix-matrix multiplication and 2D fast Fourier transform, on a heterogeneous multi-accelerator NUMA node containing an Intel multicore Haswell CPU, an Nvidia K40c GPU, and an Intel Xeon Phi co-processor and a simulated homogeneous cluster of such nodes.

A Novel Data-Partitioning Algorithm for Performance Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms

Modern HPC platforms have become highly heterogeneous owing to tight integration of multicore CPUs and accelerators (such as Graphics Processing Units, Intel Xeon Phis, or Field-Programmable Gate Arrays) empowering them to maximize the dominant objectives of performance and energy efficiency. Due to this inherent characteristic, processing elements contend for shared on-chip resources such as Last Level Cache (LLC), interconnect, etc. and shared nodal resources such as DRAM, PCI-E links, etc. This has resulted in severe resource contention and Non-Uniform Memory Access (NUMA) that have posed serious challenges to model and algorithm developers. Moreover, the accelerators feature limited main memory compared to the multicore CPU host and are connected to it via limited bandwidth PCI-E links thereby requiring support for efficient out-of-card execution. To summarize, the complexities (resource contention, NUMA, accelerator-specific limitations, etc.) have introduced new challenges to optimization of data-parallel applications on these platforms for performance. Due to these complexities, the performance profiles of data-parallel applications executing on these platforms are not smooth and deviate significantly from the shapes that allowed state-of-the-art load-balancing algorithms to find optimal solutions. In this paper, we formulate the problem of optimization of data-parallel applications on modern heterogeneous HPC platforms for performance. We then propose a new model-based data partitioning algorithm, which minimizes the execution time of computations in the parallel execution of the application. This algorithm takes as input a set of $p$ discrete speed functions corresponding to $p$ available heterogeneous processors. It does not make any assumptions about the shapes of these functions. We prove the correctness of the algorithm and its complexity of $O(m^3 \times p^3)$ , where $m$ is the cardinality of the input discrete speed functions. We experimentally demonstrate the optimality and efficiency of our algorithm using two data-parallel applications, matrix multiplication and fast Fourier transform, on a heterogeneous cluster of nodes where each node contains an Intel multicore Haswell CPU, an Nvidia K40c GPU, and an Intel Xeon Phi co-processor.

Design of self‐adaptable data parallel applications on multicore clusters automatically optimized for performance and energy through load distribution

SummarySelf‐adaptability is a highly preferred feature in HPC applications. A crucial building block of a self‐adaptable application is a data partitioning algorithm that must possess several essential qualities apart from low runtime and memory costs. On modern platforms composed of multicore CPU processors, data partitioning algorithms striving to solve the bi‐objective optimization problem for performance and energy (BOPPE) face a formidable challenge. They must take into account the new complexities inherent in these platforms such as severe resource contention and non‐uniform memory access (NUMA). Novel model‐based methods and data partitioning algorithms have been proposed that address the challenge. However, these methods take as input full functional performance and energy models (FPM and FEM), which have prohibitively high model construction costs. Therefore, they are not suitable for employment in self‐adaptable applications. In this paper, we present a self‐adaptable data partitioning algorithm called ADAPTALEPH, which solves BOPPE on homogeneous clusters of multicore CPUs. Unlike the state‐of‐the‐art solving BOPPE that take as inputs full FPM and FEM, it constructs partial FPM and FEM during its execution using all the available processors. It returns a locally Pareto‐optimal set of solutions, which are the heterogeneous workload distributions that achieve inter‐node optimization of data‐parallel applications for performance and energy. We experimentally study the efficiency of ADAPTALEPH for three data‐parallel applications, ie, matrix‐vector multiplication, matrix‐matrix multiplication, and fast Fourier transform, on a modern multicore CPU and simulations for homogeneous clusters of such CPUs. We demonstrate that the locally Pareto‐optimal front approaches the globally Pareto‐optimal front as the number of points in the partial discrete FPM and FEM functions are increased. The number of points in the partial FPM/FEM when the locally Pareto‐optimal front becomes the globally Pareto‐optimal front is considerably less than the number of points in the full FPM/FEM thereby suggesting development of methods that can leverage this finding to drastically reduce the model construction times.

A method for autonomous data partitioning

In this paper, we propose a fully autonomous, local-modes-based data partitioning algorithm, which is able to automatically recognize local maxima of the data density from empirical observations and use them as focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. The method is free from user- and problem- specific parameters and prior assumptions. The proposed algorithm has two versions: i) offline for static data and ii) evolving for streaming data. Numerical results based on benchmark datasets prove the validity of the proposed algorithm and demonstrate its excellent performance and high computational efficiency compared with the state-of-art clustering algorithms.