A Tolerance Concept in Data Clustering
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.
Unable to display preview. Download preview PDF.
- 1.Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, pp. 226–231 (1996)Google Scholar
- 2.Fisher, D.: Knowledge Acquisition Via Incremental Concept Clustering. Machine Learning 2, 139–172 (1987)Google Scholar
- 4.Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)Google Scholar
- 7.University of California at Irvine, Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLRepository.html