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공공누리This item is licensed Korea Open Government License

Title
An effective dissimilarity measure for clustering of high-dimensional categorical data
Author(s)
이정훈이윤준
Publication Year
2014-03-01
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.
Keyword
Similarity; Dissimilarity; Clustering; High-dimensional data; Categorical data
Journal Title
Knowledge and Information Systems
Citation Volume
38
ISSN
0219-1377
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Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/14345
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART68728514
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