A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic roguth set model
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Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
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ACM SIGMOD Record
A unified framework for model-based clustering
The Journal of Machine Learning Research
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IEEE Transactions on Knowledge and Data Engineering
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A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
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Bioinformatics
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Probabilistic rough set approximations
International Journal of Approximate Reasoning
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Informatica
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Information Sciences: an International Journal
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WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
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RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
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Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
Probabilistic model criteria with decision-theoretic rough sets
Information Sciences: an International Journal
Determination of the threshold value β of variable precision rough set by fuzzy algorithms
International Journal of Approximate Reasoning
Attribute reduction in decision-theoretic rough set model: a further investigation
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
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Pattern Recognition
Least squares quantization in PCM
IEEE Transactions on Information Theory
An information-theoretic interpretation of thresholds in probabilistic rough sets
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
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Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
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International Journal of Approximate Reasoning
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International Journal of Cognitive Informatics and Natural Intelligence
Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets
International Journal of Approximate Reasoning
Generalized probabilistic approximations of incomplete data
International Journal of Approximate Reasoning
Incorporating logistic regression to decision-theoretic rough sets for classifications
International Journal of Approximate Reasoning
Multigranulation decision-theoretic rough sets
International Journal of Approximate Reasoning
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International Journal of Approximate Reasoning
On an optimization representation of decision-theoretic rough set model
International Journal of Approximate Reasoning
Multigranulation decision-theoretic rough sets
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
Feature selection with test cost constraint
International Journal of Approximate Reasoning
Information Sciences: an International Journal
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Clustering provides a common means of identifying structure in complex data, and there is renewed interest in clustering as a tool for the analysis of large data sets in many fields. Determining the number of clusters in a data set is one of the most challenging and difficult problems in cluster analysis. To combat the problem, this paper proposes an efficient automatic method by extending the decision-theoretic rough set model to clustering. A new clustering validity evaluation function is designed based on the risk calculated by loss functions and possibilities. Then a hierarchical clustering algorithm, ACA-DTRS algorithm, is proposed, which is proved to stop automatically at the perfect number of clusters without manual interference. Furthermore, a novel fast algorithm, FACA-DTRS, is devised based on the conclusion obtained in the validation of the ACA-DTRS algorithm. The performance of algorithms has been studied on some synthetic and real world data sets. The algorithm analysis and the results of comparison experiments show that the new method, without manual parameter specified in advance, is more valid to determine the number of clusters and more efficient in terms of time cost.