Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Default knowledge and measures of specificity
Information Sciences: an International Journal
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Algorithms for Model-Based Gaussian Hierarchical Clustering
SIAM Journal on Scientific Computing
ACM Computing Surveys (CSUR)
Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
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
IEEE Transactions on Evolutionary Computation
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Fuzzy clustering with volume prototypes and adaptive cluster merging
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data
Knowledge-Based Systems
PCA-based high-dimensional noisy data clustering via control of decision errors
Knowledge-Based Systems
A sample-based hierarchical adaptive K-means clustering method for large-scale video retrieval
Knowledge-Based Systems
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In this paper an adaptive hierarchical fuzzy clustering algorithm is presented, named Hierarchical Data Divisive Soft Clustering (H2D-SC). The main novelty of the proposed algorithm is that it is a quality driven algorithm, since it dynamically evaluates a multi-dimensional quality measure of the clusters to drive the generation of the soft hierarchy. Specifically, it generates a hierarchy in which each node is split into a variable number of sub-nodes, determined by an innovative quality assessment of soft clusters, based on the evaluation of multiple dimensions such as the cluster's cohesion, its cardinality, its mass, and its fuzziness, as well as the partition's entropy. Clusters at the same hierarchical level share a minimum quality value: clusters in the lower levels of the hierarchy have a higher quality; this way more specific clusters (lower level clusters) have a higher quality than more general clusters (upper level clusters). Further, since the algorithm generates a soft partition, a document can belong to several sub-clusters with distinct membership degrees. The proposed algorithm is divisive, and it is based on a combination of a modified bisecting K-Means algorithm with a flat soft clustering algorithm used to partition each node. The paper describes the algorithm and its evaluation on two standard collections.