International Journal of Man-Machine Studies
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Cluster Validation with Generalized Dunn's Indices
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Some refinements of rough k-means clustering
Pattern Recognition
Web Intelligence and Agent Systems
Rough Cluster Quality Index Based on Decision Theory
IEEE Transactions on Knowledge and Data Engineering
A new fuzzy clustering algorithm for optimally finding granular prototypes
International Journal of Approximate Reasoning
Decision-theoretic rough set models
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Applications of rough set based K-means, Kohonen SOM, GA clustering
Transactions on rough sets VII
IEEE Transactions on Pattern Analysis and Machine Intelligence
On constructing clusters from non-euclidean dissimilarity matrix by using rough clustering
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Rough k-means clustering describes uncertainty by assigning some objects to more than one cluster. Rough cluster quality index based on decision theory is applicable to the evaluation of rough clustering. In this paper we analyze rough k-means clustering with respect to the selection of the threshold, the value of risk for assigning an object and uncertainty of objects. According to the analysis, clusters presented as interval sets with lower and upper approximations in rough k-means clustering are not adequate to describe clusters. This paper proposes an interval set clustering based on decision theory. Lower and upper approximations in the proposed algorithm are hierarchical and constructed as outer-level approximations and inner-level ones. Uncertainty of objects in out-level upper approximation is described by the assignment of objects among different clusters. Accordingly, ambiguity of objects in inner-level upper approximation is represented by local uniform factors of objects. In addition, interval set clustering can be improved to obtain a satisfactory clustering result with the optimal number of clusters, as well as optimal values of parameters, by taking advantage of the usefulness of rough cluster quality index in the evaluation of clustering. The experimental results on synthetic and standard data demonstrate how to construct clusters with satisfactory lower and upper approximations in the proposed algorithm. The experiments with a promotional campaign for the retail data illustrates the usefulness of interval set clustering for improving rough k-means clustering results.