Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/12893
Title: Self-optimal clustering technique using optimized threshold function
Authors: Verma, N.K.
Roy, A.
Issue Date: 2014
Citation: IEEE Systems Journal, 2014, Vol.8, 4, pp.1213-1226
Abstract: This paper presents a self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. The proposed clustering technique is equipped with major changes and modifications in its previous versions of algorithm. SOC is compared with some of the widely used clustering techniques such as K-means, fuzzy C-means, Expectation and Maximization, and K-medoid. Also, the comparison of the proposed technique is shown with IMC and its last updated version. The quantitative and qualitative performances of all these well-known clustering techniques are presented and compared with the aid of case studies and examples on various benchmarked validation indices. SOC has been evaluated via cluster compactness within itself and separation with other clusters. The optimizing factor in the threshold function is computed via interpolation and found to be effective in forming better quality clusters as verified by visual assessment and various standard validation indices like the global silhouette index, partition index, separation index, and Dunn index. 2007-2012 IEEE.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/12893
Appears in Collections:1. Journal Articles

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