{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T11:30:09Z","timestamp":1648812609091},"reference-count":20,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2002,10]]},"abstract":" In this paper we present a genetically enhanced version of the classical fuzzy c-means clustering algorithm. Our algorithm uses an evolutionary method to find optimal values for some scaling constants which are used to scale the various dimensions of the given data set so that clusters can be more easily detected by compensating for differences in distributions among features. We demonstrate how using un-scaled data with the conventional fuzzy c-means algorithm can lead to incorrect classification and how our algorithm overcomes the problem. We present the results of applying our method to both a synthetic data set, which we created to demonstrate the problem, and the standard Iris data set. In both cases, reduction of misclassifications was obtained by the new method, demonstrating improvement over the standard fuzzy c-means algorithm. <\/jats:p>","DOI":"10.1142\/s0219467802000846","type":"journal-article","created":{"date-parts":[[2002,11,25]],"date-time":"2002-11-25T10:16:49Z","timestamp":1038219409000},"page":"557-572","source":"Crossref","is-referenced-by-count":1,"title":["FUZZY CLUSTERING WITH GENETICALLY ADAPTIVE SCALING"],"prefix":"10.1142","volume":"02","author":[{"given":"ADAM","family":"SCHENKER","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Florida, 4202 E. Fowler Ave. Tampa, Florida 33620, USA"}]},{"given":"MARK","family":"LAST","sequence":"additional","affiliation":[{"name":"Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel"}]},{"given":"HORST","family":"BUNKE","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Inst. Fur Informatik und angewandte Mathematik, University of Bern, Neubr\u00fcckstrasse 10, CH-3012 Bern, Switzerland"}]},{"given":"ABRAHAM","family":"KANDEL","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Florida, 4202 E. 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