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Information Processing and Management: an International Journal
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Bayesian classification (AutoClass): theory and results
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Algorithms for Model-Based Gaussian Hierarchical Clustering
SIAM Journal on Scientific Computing
Bringing order to the Web: automatically categorizing search results
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Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Model-Based Hierarchical Clustering
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Textual information retrieval with user profiles using fuzzy clustering and inferencing
Intelligent exploration of the web
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Journal of the American Society for Information Science and Technology
The cluster-abstraction model: unsupervised learning of topic hierarchies from text data
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Supporting Search Result Browsing and Exploration via Cluster-Based Views and Zoom-Based Navigation
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Information Processing and Management: an International Journal
Computing text semantic relatedness using the contents and links of a hypertext encyclopedia
Artificial Intelligence
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In this paper an original soft hierarchical Fuzzy Clustering algorithm is proposed, named Hierarchical Hyper-spherical Divisive Fuzzy C-Means (H2D-FCM), with the following characteristics: it generates a “soft” hierarchy in which a document can belong to several child clusters of a node, and the clusters in the same hierarchical level are more specific (general) than the clusters in the upper (lower) level. The proposed algorithm is a divisive algorithm based on a modified bisective K-Means, applying a modified probabilistic Fuzzy C Means algorithm to divide each node into child-nodes. The algorithm determines the proper number of cluster to generate at the first level based on an entropy measure and decides if a node can be further split based on a “density” measure. The paper presents the algorithm and its evaluations on two standard collections.