Authors:
S Govinda Rao,N V Ganapathi Raju,A Sai Hanuman,P Varaprasada Rao,DOI NO:
https://doi.org/10.26782/jmcms.spl.3/2019.09.00014Keywords:
K-Means,Modified K-Means,Hierarchical Clustering,DDF,Modern DDF,Abstract
DDF is the most valuable measure among various cluster performance techniques to evaluate the perfectness of any cluster mechanism. Normally, best clusters are evaluated by computing the number of data points within a cluster. When this count is equivalent to the number of required data points then this cluster is considered to be perfect. The excellence of the cluster methodology is essential not only to find the data count inside a cluster but also to examine it by totaling the data points these are (i) present within a cluster where it should not be and vice versa and (ii) not clustered i.e. outliers (OL). The main functionality of DDF is that all cluster points can be grouped in similar clusters without outliers, the present paper highlights on how compared to DDF more efficient Clusters can be formed through the Modern DDF. Further, we evaluate the performance of some clustering algorithms, K-Means. Recently we developed the Modified K-Means Algorithm and Hierarchical Algorithm by using the Data Discrepancy Factor (DDF).Refference:
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