An efficient clustering algorithm based on Z-Score ranking method

Author

Kathiresan, V. ; Sumathi, Dr P.

Author_Institution

Dept. of MCA, RVS Coll. of Arts & Sci., Coimbatore, India

fYear

2012

fDate

10-12 Jan. 2012

Firstpage

1

Lastpage

4

Abstract

In this paper, we propose an algorithm to compute initial cluster centers for K-means clustering based on Z-Score ranking. This scoring technique is a statistical method of ranking numerical and nominal attributes based on distance measure. The data are sorted based on the score values. Then divide the ranked data into k subsets. Calculate the mean values of each k subsets. Pick the nearby value of data to the mean as the initial centroid. The experimental results suggest that the proposed algorithm is effective, converge to better clustering results than those of the random initialization method. The research also indicated the proposed algorithm would greatly improve the likelihood of every cluster containing some data in it.

Keywords

data mining; pattern clustering; random processes; statistical analysis; data mining technique; distance measure; initial cluster center computation; k-means clustering algorithm; nominal attribute ranking; numerical attribute ranking; random initialization method; scoring technique; statistical method; z-score ranking method; Accuracy; Algorithm design and analysis; Clustering algorithms; Convergence; Data mining; Machine learning algorithms; Partitioning algorithms; Centroid Initialization; Clustering Algorithm; K Medoid Clustering; K-means Clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Communication and Informatics (ICCCI), 2012 International Conference on

Conference_Location

Coimbatore

Print_ISBN

978-1-4577-1580-8

Type

conf

DOI

10.1109/ICCCI.2012.6158779

Filename

6158779