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Experimental results on both synthetic and real data sets show that WFPCM can save significant memory usage when comparing with the fuzzy c-means (FCM) algorithm and the possibilistic c-means (PCM) algorithm. Furthermore, the proposed algorithm is of an excellent immunity to noise and can avoid splitting or merging the exact clusters into some inaccurate clusters, and ensures the integrity and purity of the natural classes.<\/p>","DOI":"10.4018\/jdwm.2012100104","type":"journal-article","created":{"date-parts":[[2013,1,29]],"date-time":"2013-01-29T18:56:47Z","timestamp":1359485807000},"page":"82-107","source":"Crossref","is-referenced-by-count":3,"title":["Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets"],"prefix":"10.4018","volume":"8","author":[{"given":"Renxia","family":"Wan","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Tongji University, Shanghai, China & College of Information and Computation Science, Beifang University of Nationalities, Yinchuan, Ningxia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuelin","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Information and Computation Science, Beifang University of Nationalities, Yinchuan, Ningxia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caixia","family":"Li","sequence":"additional","affiliation":[{"name":"Information Office, Donghua University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jdwm.2012100104-0","doi-asserted-by":"crossref","unstructured":"Aggarwal, A., Deshpande, A., & Kannan, R. 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