Document Type
Conference Proceeding
Publication Date
12-2009
Abstract
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.
Repository Citation
Liu, Q.,
& Dong, G.
(2009). A Contrast Pattern Based Clustering Quality Index for Categorical Data. Proceedings of the Ninth IEEE International Conference on Data Mining, 860-865.
https://corescholar.libraries.wright.edu/knoesis/376
DOI
10.1109/ICDM.2009.105
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
This article was posted with permission from IEEE.