Abstract
This paper studies an abstract data clustering model, in which the similarity is explicitly represented by a tolerance relation. Three basic types of clusters are defined from each tolerance relation: maximal complete similarity clusters, representative clusters, and closure clusters. Heuristic methods of computing corresponding clusterings are introduced and an experiment on two real-world datasets are discussed. This paper provides a different view in the study of data clustering, where clusters are derived from a given similarity and different clusters may have non-empty intersection.
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Tzeng, CH., Sun, FS. (2003). Data Clustering in Tolerance Space. In: R. Berthold, M., Lenz, HJ., Bradley, E., Kruse, R., Borgelt, C. (eds) Advances in Intelligent Data Analysis V. IDA 2003. Lecture Notes in Computer Science, vol 2810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45231-7_28
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DOI: https://doi.org/10.1007/978-3-540-45231-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40813-0
Online ISBN: 978-3-540-45231-7
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