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Scalable algorithms for clustering large datasets with mixed type attributes
Author(s) -
He Zengyou,
Xu Xiaofei,
Deng Shengchun
Publication year - 2005
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20108
Subject(s) - categorical variable , cluster analysis , computer science , data mining , scalability , algorithm , data type , machine learning , artificial intelligence , database , programming language
Clustering is a widely used technique in data mining applications for discovering patterns in underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining applications. In this article, we present two algorithms that extend the Squeezer algorithm to domains with mixed numeric and categorical attributes. The performance of the two algorithms has been studied on real and artificially generated datasets. Comparisons with other clustering algorithms illustrate the superiority of our approaches. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1077–1089, 2005.