Open AccessTwo‐phase clustering algorithm with density exploring distance measure
Author(s) -
Ma Jingjing,
Jiang Xiangming,
Gong Maoguo
Publication year - 2018
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2018.0006
Subject(s) - cluster analysis , measure (data warehouse) , determining the number of clusters in a data set , single linkage clustering , algorithm , similarity (geometry) , set (abstract data type) , data set , data mining , cluster (spacecraft) , similarity measure , correlation clustering , data point , computer science , k medians clustering , regular polygon , cure data clustering algorithm , mathematics , pattern recognition (psychology) , artificial intelligence , image (mathematics) , geometry , programming language
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.