An efficient k′-means clustering algorithm

This paper introduces k′-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate ste...

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Veröffentlicht in:Pattern recognition letters Jg. 29; H. 9; S. 1385 - 1391
1. Verfasser: Žalik, Krista Rizman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.07.2008
Elsevier
Schlagworte:
Applied sciences
Cluster number
Clustering analysis
Cost-function
Exact sciences and technology
Information, signal and communications theory
k-Means
Rival penalized
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Clustering analysis
Rival penalized
Cluster number
Cost-function
k-Means
Cluster analysis
Performance evaluation
Cost minimization
Automatic classification
Iterative method
K means algorithm
Signal classification
Mean square error
Cost function
ISSN:0167-8655, 1872-7344
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Beschreibung
Zusammenfassung:This paper introduces k′-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps. The first is a pre-processing procedure that performs initial clustering and assigns at least one seed point to each cluster. During the second step, the seed-points are adjusted to minimize the cost-function. The algorithm automatically penalizes any possible winning chances for all rival seed-points in subsequent iterations. When the cost-function reaches a global minimum, the correct number of clusters is determined and the remaining seed points are located near the centres of actual clusters. The simulated experiments described in this paper confirm good performance of the proposed algorithm.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2008.02.014