A1 Journal article – refereed
Clustering in large data sets with the limited memory bundle method




List of Authors: Napsu Karmitsa, Adil M. Bagirov, Sona Taheri
Publisher: ELSEVIER SCI LTD
Publication year: 2018
Journal: Pattern Recognition
Journal name in source: PATTERN RECOGNITION
Journal acronym: PATTERN RECOGN
Volume number: 83
Number of pages: 15
ISSN: 0031-3203
eISSN: 1873-5142

Abstract
The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets. First, the clustering problem is formulated as a nonsmooth optimization problem. Then the limited memory bundle method [Haarala et al., 2007] is modified and combined with an incremental approach to design a new clustering algorithm. The algorithm is evaluated using real world data sets with both the large number of attributes and the large number of data points. It is also compared with some other optimization based clustering algorithms. The numerical results demonstrate the efficiency of the proposed algorithm for clustering in very large data sets.

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Last updated on 2019-20-07 at 15:55