Open Access Green: Postprint online available 05/2020 as soon as is submitted to ZB.
Clustering of mixed-type data considering concept hierarchies.
Lecture Notes Comp. Sci. 11439 LNAI, 555-573 (2019)
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Most clustering algorithms have been designed only for pure numerical or pure categorical data sets while nowadays many applications generate mixed data. It arises the question how to integrate various types of attributes so that one could efficiently group objects without loss of information. It is already well understood that a simple conversion of categorical attributes into a numerical domain is not sufficient since relationships between values such as a certain order are artificially introduced. Leveraging the natural conceptual hierarchy among categorical information, concept trees summarize the categorical attributes. In this paper we propose the algorithm ClicoT (CLustering mixed-type data Including COncept Trees) which is based on the Minimum Description Length (MDL) principle. Profiting of the conceptual hierarchies, ClicoT integrates categorical and numerical attributes by means of a MDL based objective function. The result of ClicoT is well interpretable since concept trees provide insights of categorical data. Extensive experiments on synthetic and real data set illustrate that ClicoT is noise-robust and yields well interpretable results in a short runtime.
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Publication type Article: Journal article
Document type Scientific Article
ISSN (print) / ISBN 0302-9743
Conference Title 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
Conference Date 14-17 April 2019
Conference Location Macau; China
Quellenangaben Volume: 11439 LNAI, Pages: 555-573
Publishing Place Berlin [u.a.]
Institute(s) Institute of Computational Biology (ICB)