D. T. Pham, S. Dimov, C. D. Nguyen
tlooto Summary
Existing methods for selecting the number of clusters for the K-means algorithm are reviewed and a new measure to assist the selection is proposed.
Abstract
The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets.
Citation format
PHAM, D. T.; DIMOV, S.; NGUYEN, C. D. Selection of k in k-means clustering. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2005, 219: 103–119.