- Title
- A new modified global k-means algorithm for clustering large data sets
- Creator
- Bagirov, Adil; Ugon, Julien; Webb, Dean
- Date
- 2009
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/58265
- Identifier
- vital:3555
- Identifier
- ISBN:9789955284635
- Abstract
- The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, in order to resolve difficulties with the choice of starting points, incremental approaches have been developed. The modified global k-means algorithm is based on such an approach. It iteratively adds one cluster center at a time. Numerical experiments show that this algorithm considerably improve the k-means algorithm. However, this algorithm is not suitable for clustering very large data sets. In this paper, a new version of the modified global k-means algorithm is proposed. We introduce an auxiliary cluster function to generate a set of starting points spanning different parts of the data set. We exploit information gathered in previous iterations of the incremental algorithm to reduce its complexity.
- Publisher
- Vilnius, Lithuania : Vilnius Gediminas Technical University Publishing House
- Relation
- Paper presented at XIIIth International Conference : Applied Stochastic Models and Data Analysis, ASMDA 2009, Vilnius, Lithuania : 30th June - 3rd July 2009 p. 1-5
- Rights
- Open Access
- Rights
- Copyright Institute of Mathematics and Informatics & Vilnius Gediminas Technical University
- Rights
- This metadata is freely available under a CCO license
- Subject
- Clustering; Nonsmooth optimization; K-means algorithm; Global k-means
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