期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2011
卷号：3
期号：12
页码：3800-3806
出版社：Engg Journals Publications
摘要：Since clustering is applied in many fields, a number of clustering techniques and algorithms have been proposed and are available in the literature. This paper proposes a novel approach to address the major problems in any of the partitional clustering algorithms like choosing appropriate K-value and selection of K-initial seed points. The performance of any partitional clustering algorithms depends on initial seed points which are random in all the existing partitional clustering algorithms. To overcome this problem, a novel algorithm called Weighted Interior Clustering (WIC) algorithm to find approximate initial seed-points, number of clusters and data points in the clusters is proposed in this paper. This paper also proposes another novel approach combining a newly proposed WIC algorithm with K-means named as Weighted Interior K-means Clustering (WIKC). The novelty of this WIKC is that it improves the quality and performance of K-means clustering algorithm with reduced complexity. The experimental results on various datasets, with various instances clearly indicates the efficacy of the proposed methods over the other methods.
关键词：Clustering; partitioning; data mining; unsupervised learning; hierarchical clustering; kmeans.
