A1 Refereed original research article in a scientific journal
New diagonal bundle method for clustering problems in large data sets
Authors: Karmitsa N, Bagirov AM, Taheri S
Publisher: ELSEVIER SCIENCE BV
Publication year: 2017
Journal: European Journal of Operational Research
Journal name in source: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Journal acronym: EUR J OPER RES
Volume: 263
Issue: 2
First page : 367
Last page: 379
Number of pages: 13
ISSN: 0377-2217
eISSN: 1872-6860
DOI: https://doi.org/10.1016/j.ejor.2017.06.010
Abstract
Clustering is one of the most important tasks in data mining. Recent developments in computer hardware allow us to store in random access memory (RAM) and repeatedly read data sets with hundreds of thousands and even millions of data points. This makes it possible to use conventional clustering algorithms in such data sets. However, these algorithms may need prohibitively large computational time and fail to produce accurate solutions. Therefore, it is important to develop clustering algorithms which are accurate and can provide real time clustering in large data sets. This paper introduces one of them. Using nonsmooth optimization formulation of the clustering problem the objective function is represented as a difference of two convex (DC) functions. Then a new diagonal bundle algorithm that explicitly uses this structure is designed and combined with an incremental approach to solve this problem. The method is evaluated using real world data sets with both large number of attributes and large number of data points. The proposed method is compared with two other clustering algorithms using numerical results. (C) 2017 Elsevier B.V. All rights reserved.
Clustering is one of the most important tasks in data mining. Recent developments in computer hardware allow us to store in random access memory (RAM) and repeatedly read data sets with hundreds of thousands and even millions of data points. This makes it possible to use conventional clustering algorithms in such data sets. However, these algorithms may need prohibitively large computational time and fail to produce accurate solutions. Therefore, it is important to develop clustering algorithms which are accurate and can provide real time clustering in large data sets. This paper introduces one of them. Using nonsmooth optimization formulation of the clustering problem the objective function is represented as a difference of two convex (DC) functions. Then a new diagonal bundle algorithm that explicitly uses this structure is designed and combined with an incremental approach to solve this problem. The method is evaluated using real world data sets with both large number of attributes and large number of data points. The proposed method is compared with two other clustering algorithms using numerical results. (C) 2017 Elsevier B.V. All rights reserved.
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