Paper Info
Title
An effective dissimilarity measure for clustering of high-dimensional categorical data.
Abstract
Clustering is to group similar data and find out hidden information about the characteristics of dataset for the further analysis. The concept of dissimilarity of objects is a decisive factor for good quality of results in clustering. When attributes of data are not just numerical but categorical and high dimensional, it is not simple to discriminate the dissimilarity of objects which have synonymous values or unimportant attributes. We suggest a method to quantify the level of difference between categorical values and to weigh the implicit influence of each attribute on constructing a particular cluster. Our method exploits distributional information of data correlated with each categorical value so that intrinsic relationship of values can be discovered. In addition, it measures significance of each attribute in constructing respective cluster dynamically. Experiments on real datasets show the propriety and effectiveness of the method, which improves the results considerably even with simple clustering algorithms. Our approach does not couple with a clustering algorithm tightly and can also be applied to various algorithms flexibly.
Year
DOI
Venue
2014
10.1007/s10115-012-0599-1
Knowl. Inf. Syst.
Keywords
Field
DocType
Similarity, Dissimilarity, Clustering, Categorical data, Multi-valued data, High-dimensional data
Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Categorical variable,Artificial intelligence,Cluster analysis,Single-linkage clustering,k-medians clustering,Clustering high-dimensional data,Correlation clustering,Pattern recognition,Machine learning
Journal
Volume
Issue
ISSN
38
3
0219-3116
Citations 
PageRank 
References 
3
0.38
14
Authors
2
Name
Order
Citations
PageRank
Jeong-Hoon Lee129116.06
Yoonjoon Lee2574175.37