Optimization and Parallelization of Fuzzy Clustering Algorithm Based on the Improved Kmeans++ Clustering
In the field of big data, fuzzy clustering algorithm is one of the most widely used clustering algorithm. Although fuzzy c-means (FCM) algorithm performs well, it still is difficult to ascertain the number of clusters and is sensitive to initial clustering center. To solve these problems, an improve...
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| Published in: | IOP conference series. Materials Science and Engineering Vol. 768; no. 7; pp. 72106 - 72112 |
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| Language: | English |
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01.03.2020
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| ISSN: | 1757-8981, 1757-899X |
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| Abstract | In the field of big data, fuzzy clustering algorithm is one of the most widely used clustering algorithm. Although fuzzy c-means (FCM) algorithm performs well, it still is difficult to ascertain the number of clusters and is sensitive to initial clustering center. To solve these problems, an improved fuzzy clustering algorithm based on Spark is proposed in this article. The proposed algorithm integrates the L2 norm and uses the kmeans++ algorithm improved by the Canopy algorithm to initialize the cluster center. Experimental results show that the proposed algorithm performs well in clustering accuracy and computational performance. |
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| AbstractList | In the field of big data, fuzzy clustering algorithm is one of the most widely used clustering algorithm. Although fuzzy c-means (FCM) algorithm performs well, it still is difficult to ascertain the number of clusters and is sensitive to initial clustering center. To solve these problems, an improved fuzzy clustering algorithm based on Spark is proposed in this article. The proposed algorithm integrates the L2 norm and uses the kmeans++ algorithm improved by the Canopy algorithm to initialize the cluster center. Experimental results show that the proposed algorithm performs well in clustering accuracy and computational performance. |
| Author | Ma, Yu Cheng, Wenjuan |
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| Cites_doi | 10.1109/TPAMI.1980.4766964 10.14778/2180912.2180915 |
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| DOI | 10.1088/1757-899X/768/7/072106 |
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| References | White (MSE_768_7_072106bib11) 2015 Ling-long (MSE_768_7_072106bib4) 2018; 37 Bahmani (MSE_768_7_072106bib8) 2017; 5 Mccallum (MSE_768_7_072106bib6) 2000 Bing (MSE_768_7_072106bib3) 2017; 37 Yun-long (MSE_768_7_072106bib13) 2019; 29 Guilan (MSE_768_7_072106bib12) 2016; 36 Bezdek (MSE_768_7_072106bib1) 1980; 2 Arthur (MSE_768_7_072106bib7) 2007 Changjun (MSE_768_7_072106bib10) 2014 Zhe (MSE_768_7_072106bib2) 2019; 40 Zaharia (MSE_768_7_072106bib9) 2012 Fangyi (MSE_768_7_072106bib5) 2019; 55 |
| References_xml | – volume: 2 start-page: 1 year: 1980 ident: MSE_768_7_072106bib1 article-title: A Convergence theorem for the fuzzy ISODATA clustering algorithms[J] publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.1980.4766964 – year: 2015 ident: MSE_768_7_072106bib11 – volume: 37 start-page: 46 year: 2018 ident: MSE_768_7_072106bib4 article-title: Optimization of Fuzzy C-Means Clustering Based on Adaptive Ant Colony Algorithm[J] publication-title: Measurement & Control Technology – start-page: 141 year: 2012 ident: MSE_768_7_072106bib9 – volume: 5 start-page: 622 year: 2017 ident: MSE_768_7_072106bib8 article-title: Scalable k-means++[J] publication-title: Proceedings of the VLDB Endowment doi: 10.14778/2180912.2180915 – year: 2014 ident: MSE_768_7_072106bib10 – volume: 37 start-page: 2600 year: 2017 ident: MSE_768_7_072106bib3 article-title: Kernel fuzzy C-means clustering based on improved artificial bee colony algorithm[J] publication-title: Journal of Computer Applications – volume: 36 start-page: 342 year: 2016 ident: MSE_768_7_072106bib12 article-title: Parallel fuzzy C-means clustering algorithm in Spark[J] publication-title: Journal of Computer Applications – volume: 40 start-page: 1390 year: 2019 ident: MSE_768_7_072106bib2 article-title: Image segmentation algorithm based on improved genetic fuzzy clustering and level set[J] publication-title: Computer Engineering and Design – volume: 55 start-page: 16 year: 2019 ident: MSE_768_7_072106bib5 article-title: Fuzzy Clustering Based on Adaptive Bat Algorithm Optimization and Its Application[J] publication-title: Computer Engineering and Applications – volume: 29 start-page: 130 year: 2019 ident: MSE_768_7_072106bib13 article-title: Implementation and Application of Fuzzy Clustering Algorithm Based on Spark[J] publication-title: Computer Technology and Development – start-page: 1027 year: 2007 ident: MSE_768_7_072106bib7 article-title: k-means++:The advantages of careful seeding[C] – start-page: 169 year: 2000 ident: MSE_768_7_072106bib6 article-title: Efficient clustering of high-dimensional data sets with application to reference matching[A] |
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| Title | Optimization and Parallelization of Fuzzy Clustering Algorithm Based on the Improved Kmeans++ Clustering |
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