Abstract
In this paper, we present an indiscernibility-based clustering method that can handle relative proximity. The main benefit of this method is that it can be applied to proximity measures that do not satisfy the triangular inequality. Additionally, it may be used with a proximity matrix – thus it does not require direct access to the original data values. In the experiments we demonstrate, with the use of partially mutated proximity matrices, that this method produces good clusters even when the employed proximity does not satisfy the triangular inequality.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Berkhin, P.: Survey of Clustering Data Mining Techniques. Accrue Software Research Paper (2002) http://www.accrue.com/products/researchpapers.html
Neyman, J., Scott, E.L.: Statistical Approach to Problems of Cosmology. In: Journal of the Royal Statistical Society, Series B20, pp. 1–43 (1958)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Arnold Publishers (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hirano, S., Tsumoto, S. (2003). An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-39804-2_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20085-7
Online ISBN: 978-3-540-39804-2
eBook Packages: Springer Book Archive