Algorithms for clustering data
Algorithms for clustering data
Static and dynamic information organization with star clusters
Proceedings of the seventh international conference on Information and knowledge management
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An Incremental Approach to Building a Cluster Hierarchy
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Model-based overlapping clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Text document clustering based on frequent word meaning sequences
Data & Knowledge Engineering
A Clustering Algorithm Based on Generalized Stars
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Dynamic hierarchical compact clustering algorithm
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
An analysis of queries intended to search information for children
Proceedings of the third symposium on Information interaction in context
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In this paper, a new clustering algorithm called DynamicHierarchical Staris introduced. Our approach aims to construct a hierarchy of overlapped clusters, dealing with dynamic data sets. The experimental results on several benchmark text collections show that this method obtains smaller hierarchies than traditional algorithms while achieving a similar clustering quality. Therefore, we advocate its use for tasks that require dynamic overlapped clustering, such as information organization, creation of document taxonomies and hierarchical topic detection.