Fuzzy co-clustering algorithm for high-order heterogeneous data
In order to analyze the clustering results of high-order heterogeneous data at the overlaps of different clusters more efficiently, a fuzzy co-clustering algorithm was developed for high-order heterogeneous data (HFCC). HFCC algo-rithm minimized distances between objects and centers of clusters in e...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.06.003/ |
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| _version_ | 1841539760641802240 |
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| author | Shao-bin HUANG Xin-xin YANG Lin-shan SHEN Yan-mei LI |
| author_facet | Shao-bin HUANG Xin-xin YANG Lin-shan SHEN Yan-mei LI |
| author_sort | Shao-bin HUANG |
| collection | DOAJ |
| description | In order to analyze the clustering results of high-order heterogeneous data at the overlaps of different clusters more efficiently, a fuzzy co-clustering algorithm was developed for high-order heterogeneous data (HFCC). HFCC algo-rithm minimized distances between objects and centers of clusters in each feature space. The update rules for fuzzy memberships of objects and weights of features were derived, and then an iterative algorithm was designed for the clus-tering process. Additionally, convergence of iterative algorithm was proved. In order to estimate the number of clusters, GXB validity index was proposed by generalizing the XB validity index, which could measure the quality of high-order clustering results. Finally, experimental results show that HFCC can efficiently mine the overlapped clusters and the qualities of clustering results of HFCC are superior five classical hard high-order co-clustering algorithms. Additionally, GXB validity index can efficiently estimate the number of high-order clusters. |
| format | Article |
| id | doaj-art-b44da4195d454beb98d99b12d2f2bfd4 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b44da4195d454beb98d99b12d2f2bfd42025-01-14T06:43:28ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-06-0135152459681837Fuzzy co-clustering algorithm for high-order heterogeneous dataShao-bin HUANGXin-xin YANGLin-shan SHENYan-mei LIIn order to analyze the clustering results of high-order heterogeneous data at the overlaps of different clusters more efficiently, a fuzzy co-clustering algorithm was developed for high-order heterogeneous data (HFCC). HFCC algo-rithm minimized distances between objects and centers of clusters in each feature space. The update rules for fuzzy memberships of objects and weights of features were derived, and then an iterative algorithm was designed for the clus-tering process. Additionally, convergence of iterative algorithm was proved. In order to estimate the number of clusters, GXB validity index was proposed by generalizing the XB validity index, which could measure the quality of high-order clustering results. Finally, experimental results show that HFCC can efficiently mine the overlapped clusters and the qualities of clustering results of HFCC are superior five classical hard high-order co-clustering algorithms. Additionally, GXB validity index can efficiently estimate the number of high-order clusters.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.06.003/high-order heterogeneous dataco-clusteringfuzzy clustering |
| spellingShingle | Shao-bin HUANG Xin-xin YANG Lin-shan SHEN Yan-mei LI Fuzzy co-clustering algorithm for high-order heterogeneous data Tongxin xuebao high-order heterogeneous data co-clustering fuzzy clustering |
| title | Fuzzy co-clustering algorithm for high-order heterogeneous data |
| title_full | Fuzzy co-clustering algorithm for high-order heterogeneous data |
| title_fullStr | Fuzzy co-clustering algorithm for high-order heterogeneous data |
| title_full_unstemmed | Fuzzy co-clustering algorithm for high-order heterogeneous data |
| title_short | Fuzzy co-clustering algorithm for high-order heterogeneous data |
| title_sort | fuzzy co clustering algorithm for high order heterogeneous data |
| topic | high-order heterogeneous data co-clustering fuzzy clustering |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.06.003/ |
| work_keys_str_mv | AT shaobinhuang fuzzycoclusteringalgorithmforhighorderheterogeneousdata AT xinxinyang fuzzycoclusteringalgorithmforhighorderheterogeneousdata AT linshanshen fuzzycoclusteringalgorithmforhighorderheterogeneousdata AT yanmeili fuzzycoclusteringalgorithmforhighorderheterogeneousdata |