Quality of Cluster Index Based on Study of Decision Tree

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Author(s):
B.Rajasekhar, B. Sunil Kumar, Rajesh Vibhudi, B.V. Rama Krishna
Published Date:
December 30, 2011
Issue:
Volume 2, Issue 1
Page(s):
39 - 43
DOI:
10.7815/ijorcs.21.2011.013
Views:
4630
Downloads:
459

Keywords:
clustering, classification, decision tree, k-means
Citation:
B.Rajasekhar, B. Sunil Kumar, Rajesh Vibhudi, B.V. Rama Krishna, "Quality of Cluster Index Based on Study of Decision Tree ". International Journal of Research in Computer Science, 2 (1): pp. 39-43, December 2011. doi:10.7815/ijorcs.21.2011.013 Other Formats

Abstract

Quality of clustering is an important issue in application of clustering techniques. Most traditional cluster validity indices are geometry-based cluster quality measures. This work proposes a cluster validity index based on the decision-theoretic rough set model by considering various loss functions. Real time retail data show the usefulness of the proposed validity index for the evaluation of rough and crisp clustering. The measure is shown to help determine optimal number of clusters, as well as an important parameter called threshold in rough clustering. The experiments with a promotional campaign for the retail data illustrate the ability of the proposed measure to incorporate financial considerations in evaluating quality of a clustering scheme. This ability to deal with monetary values distinguishes the proposed decision-theoretic measure from other distance-based measures. Our proposed system validity index can also be efficient for evaluating other clustering algorithms such as fuzzy clustering.

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