A Center Initialization Method Based on Merger of Divisions of a Data Set along Each Dimension

Abstract:

this paper proposes a hierarchical division method that divides a data set into two subsets along each dimension, and merges them into a division of the data set. Then the initial cluster centers are located in dense and separate subsets of the data set, and the means of data point in these subsets are selected as the initial cluster centers. Thus a new cluster center initialization method is developed. Experiments on real data sets show that the proposed cluster center initialization method is desirable.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 1269-1272
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.1269
• Online since: July 2013
• Authors: Guang Hui Chen
• Keywords: Cluster Center Initialization, K-Means Algorithm, Merger of Divisions

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