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A Modified Fuzzy C-Means Algorithm for Natural Data Exploration
| Content Provider | CiteSeerX |
|---|---|
| Author | Thomas, Binu Wangmo, Sonam Raju, G. |
| Abstract | Abstract—In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algorithm and its extensions, we propose a modification to the c-means algorithm to overcome the limitations of it in calculating the new cluster centers and in finding the membership values with natural data. The efficiency of the new modified method is demonstrated on real data collected for Bhutan’s Gross National Happiness (GNH) program. Keywords—Adaptive fuzzy clustering, clustering, fuzzy logic, fuzzy clustering, c-means. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Fuzzy Clustering Real Data Modified Fuzzy C-means Algorithm Large Collection New Cluster Center Classical Fuzzy C-means Algorithm Natural Data Data Mining Uncertain Natural Data Natural Data Exploration Fuzzy C-means Algorithm Bhutan Gross National Happiness Keywords Adaptive Fuzzy Clustering New Modified Method Fuzzy Logic C-means Algorithm |
| Content Type | Text |
| Resource Type | Article |
