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2006 Technical Reports

A Framework for Clustering Categorical Data based on Empirical Distributions

Bill Andreopoulos, Aijun An and Xiaogang Wang

Technical Report CS-2006-01

York University

January 2006


Density-based clustering algorithms often have a solid mathematical basis. A challenge involved in applying density-based clustering to categorical data sets is that the `cube' of attribute values has no ordering defined. In this paper we propose the CEED framework for clustering categorical data based on its empirical probability distribution. CEED offers a basis for designing categorical clustering algorithms that balance the tradeoff of accuracy and speed. The advantages of CEED are: (i) it offers a probabilistic basis for clustering categorical data, (ii) it minimizes the user-specified input parameters, (iii) it is insensitive to the order of the input objects, (iv) it can discover clusters of arbitrary shapes and sizes. We present a faster approximation of CEED called the MULIC algorithm, which is designed for categorical data sets with a multi-layered structure. We evaluate CEED and MULIC on various data sets, including protein interaction data. CEED produces more accurate results than other algorithms on small-dimensional data sets. MULIC can find the multi-layered structure of special data sets such as protein interaction data better than other algorithms and has comparable runtimes.

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