| 期刊全称 | Algorithms for Fuzzy Clustering | | 期刊简称 | Methods in c-Means C | | 影响因子2023 | Sadaaki Miyamoto,Hidetomo Ichihashi,Katsuhiro Hond | | 视频video | | | 发行地址 | Presents recent advances in algorithms for fuzzy clustering | | 学科分类 | Studies in Fuzziness and Soft Computing | | 图书封面 |  | | 影响因子 | Recently many researchers are working on cluster analysis as a main tool for exploratory data analysis and data mining. A notable feature is that specialists in di?erent ?elds of sciences are considering the tool of data clustering to be useful. A major reason is that clustering algorithms and software are ?exible in thesensethatdi?erentmathematicalframeworksareemployedinthealgorithms and a user can select a suitable method according to his application. Moreover clusteringalgorithmshavedi?erentoutputsrangingfromtheolddendrogramsof agglomerativeclustering to more recent self-organizingmaps. Thus, a researcher or user can choose an appropriate output suited to his purpose,which is another ?exibility of the methods of clustering. An old and still most popular method is the K-means which use K cluster centers. A group of data is gathered around a cluster center and thus forms a cluster. The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reasonwhy we concentrate on fuzzy c-means is that most methodology and application studies infuzzy clusteringusefuzzy c-means,andfuzzy c-meansshouldbe consideredto beamajo | | Pindex | Book 2008 |
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发表于 2025-3-21 16:37:30
