Authors:
S. Rahamat Basha,M.Surya Bhupal Rao,Dr. P. Kiran Kumar Reddy,DOI NO:
https://doi.org/10.26782/jmcms.2020.08.00061Keywords:
Clustering Analysis,Cluster Accuracy,visual assessment,CCE,DBE,VAT,Abstract
Major issue in cluster analysis is determining the number of clusters present in a data set. The automated identification of the number of clusters can be satisfactorily solved with very few techniques. Recent developments have resulted in a very popular visual mechanism for clustering trend determination (VAT, Visual Assessment of Clustering Tendency) in data sets. The techniques used for image processing depend on the structure of the VAT image, without using any cluster validity concept. High speed solutions can be found in conjunction with GAs from VAT approaches. This approach however depends on the ability of the index concerned to identify overlapping clusters.We will explain how VAT algorithms can be very quickly used to correctly determine the number of clusters. The implementation of the approaches proposed by taking cluster accuracy, cluster error and computational time as metrics.Refference:
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