- Title
- An algorithm for clustering using L1-norm based on hyperbolic smoothing technique
- Creator
- Bagirov, Adil; Mohebi, Ehsan
- Date
- 2016
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160808
- Identifier
- vital:12254
- Identifier
-
https://doi.org/10.1111/coin.12062
- Identifier
- ISBN:0824-7935
- Abstract
- Cluster analysis deals with the problem of organization of a collection of objects into clusters based on a similarity measure, which can be defined using various distance functions. The use of different similarity measures allows one to find different cluster structures in a data set. In this article, an algorithm is developed to solve clustering problems where the similarity measure is defined using the L1-norm. The algorithm is designed using the nonsmooth optimization approach to the clustering problem. Smoothing techniques are applied to smooth both the clustering function and the L1-norm. The algorithm computes clusters sequentially and finds global or near global solutions to the clustering problem. Results of numerical experiments using 12 real-world data sets are reported, and the proposed algorithm is compared with two other clustering algorithms. ©2015 Wiley Periodicals, Inc.
- Publisher
- Blackwell Publishing Inc.
- Relation
- Computational Intelligence Vol. 32, no. 3 (2016), p. 439-457; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright ©2015 Wiley Periodicals, Inc.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0802 Computation Theory and Mathematics; 0806 Information Systems; Cluster analysis; Nonsmooth optimization; Similarity measure; Smoothing techniques
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