An Adaptive Fuzzy Joint Points Clustering Algorithm

doi:10.3969/j.issn.1006-2475.2019.10.011

Abstract

Abstract: Fuzzy Joint Points (FJP) divides the cluster number of clusters by the maximum interval descent method. This method of determining the number of clusters is subjective and is not conducive to the application of the algorithms. Aiming at this problem, an adaptive FJP clustering algorithm based on effective neighbor cluster index is proposed. The Kernels-VCN index is used to evaluate the effectiveness of clustering, so as to achieve the optimal determination of the optimal number of clusters. Finally, we verify the feasibility of the proposed algorithm on UCI datasets and artificial datasets.

Key words: fuzzy joint point, effective neighbor cluster index, optimal division level, optimal number of clusters

CLC Number:

TP391

WANG Bao-feng, MA Xiao-xuan, LI Jin-xing. An Adaptive Fuzzy Joint Points Clustering Algorithm[J]. Computer and Modernization, 2019, 0(10): 55-.

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