ADCLUS and INDCLUS: analysis, experimentation, and meta-heuristic algorithm extensions

Stephen L. France (), Wen Chen () and Yumin Deng ()
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Stephen L. France: Mississippi State University
Wen Chen: Providence College School of Business, Koffler Hall
Yumin Deng: Alibaba Group

Advances in Data Analysis and Classification, 2017, vol. 11, issue 2, No 8, 393 pages

Abstract: Abstract The ADCLUS and INDCLUS models, along with associated fitting techniques, can be used to extract an overlapping clustering structure from similarity data. In this paper, we examine the scalability of these models. We test the SINDLCUS algorithm and an adapted version of the SYMPRES algorithm on medium size datasets and try to infer their scalability and the degree of the local optima problem as the problem size increases. We describe several meta-heuristic approaches to minimizing the INDCLUS and ADCLUS loss functions.

Keywords: Overlapping clustering; Optimization; NP-Hard; 62-07 Data analysis; 62H30 Classification and discrimination; 91C20 Cluster analysis (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11634-016-0244-z

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