An effective dissimilarity measure for clustering of high-dimensional categorical data

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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.
Publisher
SPRINGER LONDON LTD
Issue Date
2014-03
Language
English
Article Type
Article
Keywords

ALGORITHM

Citation

KNOWLEDGE AND INFORMATION SYSTEMS, v.38, no.3, pp.743 - 757

ISSN
0219-1377
DOI
10.1007/s10115-012-0599-1
URI
http://hdl.handle.net/10203/190160
Appears in Collection
CS-Journal Papers(저널논문)
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