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A binomial noised model for cluster validation
Date
2013-01-01
Author
Toledano-Kitai, Dvora
Avros, Renata
Volkovich, Zeev
Weber, Gerhard Wilhelm
Yahalom, Orly
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Cluster validation is the task of estimating the quality of a given partition of a data set into clusters of similar objects. Normally, a clustering algorithm requires a desired number of clusters as a parameter. We consider the cluster validation problem of determining the optimal ("true") number of clusters. We adopt the stability testing approach, according to which, repeated applications of a given clustering algorithm provide similar results when the specified number of clusters is correct. To implement this idea, we draw pairs of independent equal sized samples, where one sample in any pair is drawn from the data source and the other one is drawn from a noised version thereof. We then run the same clustering method on both samples in any pair and test the similarity between the obtained partitions using a general k-Nearest Neighbor Binomial model. These similarity measurements enable us to estimate the correct number of clusters. A series of numerical experiments on both synthetic and real world data demonstrates the high capability of the offered discipline compared to other methods. In particular, the use of a noised data set is shown to produce significantly better results than in the case of using two independent samples which are both drawn from the data source.
Subject Keywords
General Engineering
,
Statistics and Probability
,
Artificial Intelligence
URI
https://hdl.handle.net/11511/57925
Journal
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
DOI
https://doi.org/10.3233/ifs-2012-0563
Collections
Graduate School of Applied Mathematics, Article