An efficient k′-means clustering algorithm

• Published in: Pattern recognition letters Vol. 29; no. 9; pp. 1385 - 1391
• Main Author: Žalik, Krista Rizman
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2008; Elsevier
• Subjects: 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

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...