Robust Interval Competitive Agglomeration Clustering Algorithm with Outliers
نویسندگان
چکیده
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.
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