Self Optimal Clustering (SOC)
Self Optimal Clustering (SOC) 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.
Please cite the following paper:
The text format is:
Nishchal K. Verma and Abhishek Roy,
Self Optimal Clustering Technique Using Optimized Threshold Function, IEEE Systems Journal, vol. 99, pp. 1-14, Jul. 2013.
Developer's code can be given on request.
Name: Dr. Nishchal K. Verma