Abstract
This paper introduces the concept of tolerance space as an abstract model of data clustering. The similarity in the model is represented by a relation with both reflexivity and symmetry, called a tolerance relation. Three types of clusterings based on a tolerance relation are introduced: maximal complete similarity clustering, representative clustering, and closure clustering. This paper also discusses experiments on unsupervised learning, in which Hamming distance is used to define a family of tolerance relations.
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Sun, FS., Tzeng, CH. (2003). A Tolerance Concept in Data Clustering. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_45
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DOI: https://doi.org/10.1007/978-3-540-45080-1_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
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