篇名 | Robust Interval Competitive Agglomeration Clustering Algorithm with Outliers |
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卷期 | 12:3 |
作者 | Jin-Tsong Jeng 、 Chen-Chia Chuang 、 Chih-Cheng Tseng 、 Chang-Jung Juan |
頁次 | 227-236 |
關鍵字 | Symbolic interval-values data 、 Robust Interval Competitive Agglomeration Clustering Algorithm 、 Interval Fuzzy c-means clustering algorithm and Outliers 、 EI 、 SCI 、 SCIE 、 Scopus |
出刊日期 | 201009 |
In this study, a novel robust clustering algorithm, robust interval competitive agglomeration (RICA) clustering algorithm, is proposed to overcome the problems of the outliers, the numbers of cluster and the initialization of prototype in the fuzzy C-means (FCM) clustering algorithm for the symbolic interval- values data. In the proposed RICA clustering algorithm, the Euclidean distance measure is considered. Due to the competitive agglomeration is used, the RICA clustering algorithm can be fast converges in a few iterations and to the same optimal partition regardless of its initialization of prototype. Experimentally results show the merits and usefulness of the RICA clustering algorithm for the symbolic interval- values data with outliers.