Incremental Fuzzy Clustering Based on Feature Reduction
In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8566253 |
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| _version_ | 1832562227539869696 |
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| author | Yongli Liu Yajun Zhang Hao Chao |
| author_facet | Yongli Liu Yajun Zhang Hao Chao |
| author_sort | Yongli Liu |
| collection | DOAJ |
| description | In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts of current clustering algorithms. Combined with single-pass and online incremental strategies, respectively, we propose two incremental fuzzy clustering algorithms based on feature reduction. The first uses the Weighted Feature Reduction Fuzzy C-Means (WFRFCM) clustering algorithm to process each chunk in turn and combines the clustering results of the previous chunk into the latter chunk for common calculation. The second uses the WFRFCM algorithm for each chunk to cluster at the same time, and the clustering results of each chunk are combined and calculated again. In order to investigate the clustering performance of these two algorithms, six datasets were selected for comparative experiments. Experimental results showed that these two algorithms could select high-quality features based on feature reduction and process large-scale data by introducing the incremental strategy. The combination of the two phases can not only ensure the clustering efficiency but also keep higher clustering accuracy. |
| format | Article |
| id | doaj-art-e285ce7249424c47a31e77601784359a |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-e285ce7249424c47a31e77601784359a2025-02-03T01:23:13ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/8566253Incremental Fuzzy Clustering Based on Feature ReductionYongli Liu0Yajun Zhang1Hao Chao2School of Computer Science and TechnologySchool of Computer Science and TechnologySchool of Computer Science and TechnologyIn the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts of current clustering algorithms. Combined with single-pass and online incremental strategies, respectively, we propose two incremental fuzzy clustering algorithms based on feature reduction. The first uses the Weighted Feature Reduction Fuzzy C-Means (WFRFCM) clustering algorithm to process each chunk in turn and combines the clustering results of the previous chunk into the latter chunk for common calculation. The second uses the WFRFCM algorithm for each chunk to cluster at the same time, and the clustering results of each chunk are combined and calculated again. In order to investigate the clustering performance of these two algorithms, six datasets were selected for comparative experiments. Experimental results showed that these two algorithms could select high-quality features based on feature reduction and process large-scale data by introducing the incremental strategy. The combination of the two phases can not only ensure the clustering efficiency but also keep higher clustering accuracy.http://dx.doi.org/10.1155/2022/8566253 |
| spellingShingle | Yongli Liu Yajun Zhang Hao Chao Incremental Fuzzy Clustering Based on Feature Reduction Journal of Electrical and Computer Engineering |
| title | Incremental Fuzzy Clustering Based on Feature Reduction |
| title_full | Incremental Fuzzy Clustering Based on Feature Reduction |
| title_fullStr | Incremental Fuzzy Clustering Based on Feature Reduction |
| title_full_unstemmed | Incremental Fuzzy Clustering Based on Feature Reduction |
| title_short | Incremental Fuzzy Clustering Based on Feature Reduction |
| title_sort | incremental fuzzy clustering based on feature reduction |
| url | http://dx.doi.org/10.1155/2022/8566253 |
| work_keys_str_mv | AT yongliliu incrementalfuzzyclusteringbasedonfeaturereduction AT yajunzhang incrementalfuzzyclusteringbasedonfeaturereduction AT haochao incrementalfuzzyclusteringbasedonfeaturereduction |