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A Contrast Pattern Based Clustering Quality Index for Categorical Data

9 years 8 months ago
A Contrast Pattern Based Clustering Quality Index for Categorical Data
Since clustering is unsupervised and highly explorative, clustering validation (i.e. assessing the quality of clustering solutions) has been an important and long standing research problem. Existing validity measures have significant shortcomings. This paper proposes a novel Contrast Pattern based Clustering Quality index (CPCQ) for categorical data, by utilizing the quality and diversity of the contrast patterns (CPs) which contrast the clusters in clusterings. High quality CPs can characterize clusters and discriminate them against each other. Experiments show that the CPCQ index (1) can recognize that expert-determined classes are the best clusters for many datasets from the UCI repository; (2) does not give inappropriate preference to larger number of clusters; (3) does not require a user to provide a distance function. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications – Data mining. General Terms Measurement. Keywords Clustering validation; c...
Qingbao Liu, Guozhu Dong
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICDM
Authors Qingbao Liu, Guozhu Dong
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