Clustering of Mixed-type Data Considering Concept Hierarchies

Author(s): Sahar Behzadi Soheil, Nikola Müller, Claudia Plant, Christian Böhm
Abstract: 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.
Organisation(s): Research Group Data Mining and Machine Learning, Research Network Data Science
External organisation(s): Helmholtz-Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Ludwig-Maximilians-Universität München
Pages: 555-573
No. of pages: 19
DOI: https://doi.org/10.1007/978-3-030-16148-4_43
Publication date: 2019
Peer reviewed: Yes
Austrian Fields of Science 2012: 102033 Data mining
Keywords
ASJC Scopus subject areas: Theoretical Computer Science, Computer Science(all)
Portal url: https://ucris.univie.ac.at/portal/en/publications/clustering-of-mixedtype-data-considering-concept-hierarchies(4b3422b6-b4ab-4335-bdeb-96ba469aa0ae).html