Fuzzy sets, decision making and expert systems
Fuzzy sets, decision making and expert systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Application of fuzzy logic to approximate reasoning using linguistic synthesis
MVL '76 Proceedings of the sixth international symposium on Multiple-valued logic
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
On the relative hardness of clustering corpora
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
An approach to clustering abstracts
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
A probabilistic majorclust variant for the clustering of near-homogeneous graphs
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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Among various document clustering algorithms that have been proposed so far, the most useful are those that automatically reveal the number of clusters and assign each target document to exactly one cluster. However, in many real situations, there not exists an exact boundary between different clusters. In this work, we introduce a fuzzy version of the MajorClust algorithm. The proposed clustering method assigns documents to more than one category by taking into account a membership function for both, edges and nodes of the corresponding underlying graph. Thus, the clustering problem is formulated in terms of weighted fuzzy graphs. The fuzzy approach permits to decrease some negative effects which appear in clustering of large-sized corpora with noisy data.