Finding Relationships Between the Our-NIR Cluster Results

The problem of evaluating node importance in clustering has been active research in presentdays and many methods have been developed. Most of the clustering algorithms deal withgeneral similarity measures. However In real situation most of the cases data changes over time.But clustering this type of data not only decreases the quality of clusters but also disregards theexpectation of users, when usually require recent clustering results. In this regard we proposedOur-NIR method that is better than Ming-Syan Chen proposed a method and it has proven withthe help of results of node importance, which is related to calculate the node importance that isvery useful in clustering of categorical data, which is for evaluating of node importance byintroducing the probability distribution which will be better than by comparing the results .That isdetects drifting concepts and try to show the evolving clustering results in the categorical domain.This scheme is based on the cosine measure that analyzes relationship between clusteringresults at different time stamps using Our-NIR method