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INVESTIGATION OF COMBINATIONAL CLUSTERING INDICES IN ARTIFICIAL IMMUNE MULTI‐OBJECTIVE CLUSTERING
Author(s) -
Yang Dongdong,
Jiao Licheng,
Niu Ruican,
Gong Maoguo
Publication year - 2014
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2012.00466.x
Subject(s) - cluster analysis , computer science , artificial intelligence , cure data clustering algorithm , correlation clustering , canopy clustering algorithm , pattern recognition (psychology) , benchmark (surveying) , artificial immune system , data stream clustering , data mining , machine learning , geodesy , geography
Clustering validity indices play a core role in the unsupervised pattern classification. To date, some indices have been proposed and their individual performances were compared on different artificial clustering test instances and real image segmentation tasks. However, little focus has been placed on the issue that how about their combinational performances are, although multi‐objective optimization has attracted much interest from current researchers in unsupervised classification. Here, we firstly evaluate the performance of five state‐of‐the‐art clustering indices with different characteristics in a single‐objective optimization algorithm designed in artificial immune system (AIS). Then, a multi‐objective optimization algorithm in AIS with fair ability of adaptability and diversity maintaining is introduced in this study. After that, the clustering performances of combinational clustering indices are investigated in the multi‐objective optimization framework. To test the effectiveness of the combinational clustering validity indices, 27 benchmark functions with different geometric structures and three complicated remote sensing images are employed in this study. Based on the computer simulations, some meaningful empirical guidelines are obtained for selecting the suitable combinational clustering indices for formulating an effective and robust multi‐objective clustering algorithm in different clustering tasks.