Automatic Clustering Approaches Based On Initial Seed Points
Journal Title: International Journal on Computer Science and Engineering - Year 2011, Vol 3, Issue 12
Abstract
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.
Authors and Affiliations
G. V. S. N. R. V. Prasad , V. Venkata Krishna , V. Vijaya Kumar
Keywords
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- EP ID EP108355
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