| タイトル | A possibilistic approach to clustering |
| 著者（英） | Keller, James M.; Krishnapuram, Raghu |
| 著者所属（英） | NASA Headquarters |
| 発行日 | 1993-05-01 |
| 言語 | eng |
| 内容記述 | Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise. |
| NASA分類 | CYBERNETICS |
| レポートNO | 93A50719 |
| 権利 | Copyright |
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