Our - NIR : Node Importance Representative for Clustering of Categorical Data

Abstract

The problem of evaluating node importance in clustering has been active research in present days and many methods have been developed. Most of the clustering algorithms deal with general 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 the expectation of users, when usually require recent clustering results. In this regard Ming-Syan Chen proposed a method, which is related to calculate the node importance that is very useful in clustering of categorical data, but it has serious deficiency that is bias towards features with many outcomes. In this paper we proposed a new method evaluating of node importance by summarize rules which will be better than the Ming-Syan Chen proposed method by comparing the results.

4 Figures and Tables

Cite this paper

@inproceedings{Chen2011OurN, title={Our - NIR : Node Importance Representative for Clustering of Categorical Data}, author={Ming-Syan Chen}, year={2011} }