A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
A Knowledge-Oriented Clustering Technique Based on Rough Sets
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
A Method of Web Search Result Clustering Based on Rough Sets
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Hierarchical Adaptive Clustering
Informatica
Learning Optimal Parameters in Decision-Theoretic Rough Sets
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Rough Cluster Quality Index Based on Decision Theory
IEEE Transactions on Knowledge and Data Engineering
Three-way decisions with probabilistic rough sets
Information Sciences: an International Journal
Carrot2 and language properties in web search results clustering
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
A unifying abstract approach for rough models
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
The superiority of three-way decisions in probabilistic rough set models
Information Sciences: an International Journal
A novel possibilistic fuzzy leader clustering algorithm
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Rough set Based Ensemble Classifier forWeb Page Classification
Fundamenta Informaticae
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
Hi-index | 0.00 |
In many applications, clusters tend to have vague or imprecise boundaries. It is desirable that clustering techniques should consider such an issue. The decision-theoretic rough set (DTRS) model is a typical probabilistic rough set model, which has the ability to deal with imprecise, uncertain, and vague information. This paper proposes an autonomous clustering method using the decision-theoretic rough set model based on a knowledge-oriented clustering framework. In order to get the initial knowledge-oriented clustering, the threshold values are produced autonomously based on semantics of clustering without human intervention. Furthermore, this paper estimates the risk of a clustering scheme based on the decision-theoretic rough set by considering various loss functions, which can process the different granular overlapping boundary. An autonomous clustering algorithm is proposed, which is not only experimented with the synthetic data and the standard data but also applied in the web search results clustering. The results of experiments show that the proposed method is effective and efficient.