Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing volume of the data, the variety of the number of c...
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| Veröffentlicht in: | Applied sciences Jg. 12; H. 13; S. 6464 |
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01.07.2022
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| Abstract | The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing volume of the data, the variety of the number of clusters, and their shapes. The present work aims to improve the accuracy of sequential clustering batches of data streams for scenarios in which clusters evolve dynamically and continuously, automatically estimating their number. In order to achieve this goal, three evolutionary algorithms are presented, along with three novel algorithms designed to deal with clusters of normal distribution based on goodness-of-fit tests in the context of scalable batch stream clustering with automatic estimation of the number of clusters. All of them are developed on top of MapReduce, Discretized-Stream models, and the most recent MPC frameworks to provide scalability, reliability, resilience, and flexibility. The proposed algorithms are experimentally compared with state-of-the-art methods and present the best results for accuracy for normally distributed data sets, reaching their goal. |
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| AbstractList | The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing volume of the data, the variety of the number of clusters, and their shapes. The present work aims to improve the accuracy of sequential clustering batches of data streams for scenarios in which clusters evolve dynamically and continuously, automatically estimating their number. In order to achieve this goal, three evolutionary algorithms are presented, along with three novel algorithms designed to deal with clusters of normal distribution based on goodness-of-fit tests in the context of scalable batch stream clustering with automatic estimation of the number of clusters. All of them are developed on top of MapReduce, Discretized-Stream models, and the most recent MPC frameworks to provide scalability, reliability, resilience, and flexibility. The proposed algorithms are experimentally compared with state-of-the-art methods and present the best results for accuracy for normally distributed data sets, reaching their goal. |
| Author | Silva, Jonathan Andrade Faria, Elaine Ribeiro Cândido, Paulo Gustavo Lopes Naldi, Murilo Coelho |
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| References | Naldi (ref_34) 2011; 11 Madani (ref_31) 2013; 92 ref_36 ref_13 ref_12 ref_11 Zaharia (ref_25) 2016; 59 ref_33 Aggarwal (ref_22) 2003; Volume 29 ref_32 ref_30 Meng (ref_24) 2016; 17 Hubert (ref_28) 1985; 2 Khader (ref_14) 2020; 53 ref_19 ref_18 ref_17 ref_16 ref_15 Shapiro (ref_35) 1965; 52 Morales (ref_7) 2015; 16 Gomes (ref_2) 2019; 21 Dean (ref_9) 2008; 51 Silva (ref_3) 2013; 46 ref_21 ref_20 ref_1 Massey (ref_26) 1951; 46 ref_27 Silva (ref_23) 2017; 67 ref_8 ref_5 ref_4 Bifet (ref_29) 2010; 11 ref_6 Oliveira (ref_10) 2017; 246 |
| References_xml | – volume: 59 start-page: 56 year: 2016 ident: ref_25 article-title: Apache Spark: A Unified Engine for Big Data Processing publication-title: Commun. ACM doi: 10.1145/2934664 – ident: ref_30 – ident: ref_5 – ident: ref_32 – volume: 53 start-page: 1 year: 2020 ident: ref_14 article-title: Density-Based Algorithms for Big Data Clustering Using MapReduce Framework: A Comprehensive Study publication-title: ACM Comput. Surv. doi: 10.1145/3403951 – volume: 11 start-page: 1601 year: 2010 ident: ref_29 article-title: MOA Massive Online Analysis publication-title: J. Mach. Learn. Res. – volume: 51 start-page: 107 year: 2008 ident: ref_9 article-title: MapReduce: Simplified data processing on large clusters publication-title: Commun. ACM doi: 10.1145/1327452.1327492 – ident: ref_18 – ident: ref_13 doi: 10.1109/CEC.2018.8477668 – ident: ref_19 doi: 10.7551/mitpress/7503.003.0054 – ident: ref_21 – ident: ref_8 doi: 10.1109/ICDMW.2015.140 – ident: ref_1 doi: 10.1201/EBK1439826119 – volume: 21 start-page: 6 year: 2019 ident: ref_2 article-title: Machine learning for streaming data: State of the art, challenges, and opportunities publication-title: ACM SIGKDD Explor. Newsl. doi: 10.1145/3373464.3373470 – ident: ref_6 – ident: ref_11 doi: 10.21236/ADA575859 – ident: ref_4 – ident: ref_33 – ident: ref_12 doi: 10.1109/BRACIS.2017.53 – ident: ref_27 – volume: 16 start-page: 149 year: 2015 ident: ref_7 article-title: SAMOA: Scalable Advanced Massive Online Analysis publication-title: J. Mach. Learn. Res. – volume: 11 start-page: 1938 year: 2011 ident: ref_34 article-title: Efficiency issues of evolutionary k-means publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2010.06.010 – volume: 46 start-page: 1 year: 2013 ident: ref_3 article-title: Data stream clustering publication-title: ACM Comput. Surv. doi: 10.1145/2522968.2522981 – volume: 17 start-page: 1235 year: 2016 ident: ref_24 article-title: MLlib: Machine Learning in Apache Spark publication-title: J. Mach. Learn. Res. – ident: ref_15 – volume: Volume 29 start-page: 81 year: 2003 ident: ref_22 article-title: Aggarwal, C.C.; Han, J.; Wang, J.; Yu, P.S. A framework for clustering evolving data streams publication-title: Proceedings 2003 VLDB Conference doi: 10.1016/B978-012722442-8/50016-1 – volume: 92 start-page: 457 year: 2013 ident: ref_31 article-title: On Using Nearly-Independent Feature Families for High Precision and Confidence publication-title: Mach. Learn. doi: 10.1007/s10994-013-5377-0 – ident: ref_16 doi: 10.1201/9781420089653 – ident: ref_17 – ident: ref_36 – volume: 246 start-page: 45 year: 2017 ident: ref_10 article-title: Improving k-means through distributed scalable metaheuristics publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.07.074 – volume: 67 start-page: 228 year: 2017 ident: ref_23 article-title: An evolutionary algorithm for clustering data streams with a variable number of clusters publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.09.020 – ident: ref_20 – volume: 2 start-page: 193 year: 1985 ident: ref_28 article-title: Comparing partitions publication-title: J. Classif. doi: 10.1007/BF01908075 – volume: 46 start-page: 68 year: 1951 ident: ref_26 article-title: The Kolmogorov-Smirnov Test for Goodness of Fit publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1951.10500769 – volume: 52 start-page: 591 year: 1965 ident: ref_35 article-title: An Analysis of Variance Test for Normality (Complete Samples) publication-title: Biometrika doi: 10.1093/biomet/52.3-4.591 |
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| SubjectTerms | Algorithms Big Data Clustering data stream Datasets Hypotheses Machine learning massive parallel computation Normal distribution |
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| Title | Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation |
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