Two-phase clustering algorithm with density exploring distance measure

CAAI Transactions on Intelligence Technology

article

Jingjing Ma¹  Xiangming Jiang¹  Maoguo Gong¹ 

[1] Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University

关键词: pattern clustering;    statistical distributions;    sorting;    DED measure;    data points;    UCI data sets;    comparison algorithms;    two-phase clustering algorithm;    cluster number;    density exploring distance measure;    fast global K-means clustering algorithm;    data distributions;    non-convex distribution;    C1140Z Other topics in statistics;    C1160 Combinatorial mathematics;    C6130 Data handling techniques;   

DOI  :  10.1049/trit.2018.0006

学科分类：数学（综合）

来源: Wiley

【 摘 要 】

Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance (DED) measure. In the first phase, the fast global K -means clustering algorithm is used to obtain the cluster number and the prototypes. Then, the prototypes of all these clusters and representatives of points belonging to these clusters are regarded as the input data set of the second phase. Afterwards, all the prototypes are clustered according to a DED measure which makes data points locating in the same structure to possess high similarity with each other. In experimental studies, the authors test the proposed algorithm on seven artificial as well as seven UCI data sets. The results demonstrate that the proposed algorithm is flexible to different data distributions and has a stronger ability in clustering data sets with complex non-convex distribution when compared with the comparison algorithms.

【 授权许可】

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