{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:40:28Z","timestamp":1761597628138},"reference-count":20,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Unc. Fuzz. Knowl. Based Syst."],"published-print":{"date-parts":[[2015,8]]},"abstract":"<jats:p> Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy [Formula: see text]-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concept of degree of belongingness, and PCAs suffer from the \u201ccoincident problem\u201d and cannot provide very stable results for some data sets. In this study, we propose a new clustering algorithm, called the credibilistic clustering algorithm (CCA), based on the credibility measure. The credibility measure provides some unique properties which can solve the \u201ccoincident problem\u201d and noise issue compared with the probability measure and possibility measure. Based on some randomly generated data sets, experimental results compared with FCM and PCA show that CCA can deal with the \u201ccoincident problem\u201d with good clustering results, and it is more robust to noise than PCA. <\/jats:p>","DOI":"10.1142\/s0218488515500245","type":"journal-article","created":{"date-parts":[[2015,8,14]],"date-time":"2015-08-14T03:13:26Z","timestamp":1439522006000},"page":"545-564","source":"Crossref","is-referenced-by-count":5,"title":["Credibilistic Clustering: The Model and Algorithms"],"prefix":"10.1142","volume":"23","author":[{"given":"Jian","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}]},{"given":"Qina","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}]},{"given":"C.-C.","family":"Hung","sequence":"additional","affiliation":[{"name":"Anyang Normal University, Anyang 455000, China"},{"name":"School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, GA 30060, USA"}]},{"given":"Xiajie","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Management, Shanghai University, Shanghai 200444, China"}]}],"member":"219","published-online":{"date-parts":[[2015,8,14]]},"reference":[{"key":"p_2","doi-asserted-by":"publisher","DOI":"10.1109\/91.227387"},{"key":"p_3","doi-asserted-by":"publisher","DOI":"10.1109\/91.531779"},{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488506004175"},{"key":"p_5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.09.032"},{"key":"p_6","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013771608623"},{"key":"p_9","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2002.800692"},{"key":"p_10","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-013-1513-9"},{"key":"p_11","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(78)90011-8"},{"key":"p_12","doi-asserted-by":"publisher","DOI":"10.1556\/AOecon.64.2014.2.2"},{"key":"p_13","doi-asserted-by":"publisher","DOI":"10.1109\/91.784198"},{"key":"p_14","doi-asserted-by":"publisher","DOI":"10.1080\/00207217.2013.805387"},{"key":"p_15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2007.04.017"},{"issue":"6","key":"p_16","first-page":"2035","volume":"10","author":"Wang Q.","year":"2014","journal-title":"Information and Control"},{"issue":"3","key":"p_17","first-page":"174","volume":"10","author":"Wen P.","year":"2008","journal-title":"Int. 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