Evolutionary Rough K-Means Clustering
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Two-level hierarchical combination method for text classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Autonomous knowledge-oriented clustering using decision-theoretic rough set theory
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
A multi-agent decision-theoretic rough set model
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
An improved rough clustering using discernibility based initial seed computation
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A new discriminant analysis approach under decision-theoretic rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Automatically determining the number of clusters using decision-theoretic rough set
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Autonomous Knowledge-oriented Clustering Using Decision-Theoretic Rough Set Theory
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
Incorporating logistic regression to decision-theoretic rough sets for classifications
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
An extension to Rough c-means clustering based on decision-theoretic Rough Sets model
International Journal of Approximate Reasoning
On an optimization representation of decision-theoretic rough set model
International Journal of Approximate Reasoning
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
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Quality of clustering is an important issue in application of clustering techniques. Most traditional cluster validity indices are geometry-based cluster quality measures. This paper proposes a cluster validity index based on the decision-theoretic rough set model by considering various loss functions. Experiments with synthetic, standard, and real-world 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. The proposed validity index can also be extended for evaluating other clustering algorithms such as fuzzy clustering.