Constant interaction-time scatter/gather browsing of very large document collections
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Static and dynamic information organization with star clusters
Proceedings of the seventh international conference on Information and knowledge management
Using star clusters for filtering
Proceedings of the ninth international conference on Information and knowledge management
Information Retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Model-based overlapping clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Hierarchical Star Clustering Algorithm for Dynamic Document Collections
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A New Incremental Algorithm for Overlapped Clustering
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A new overlapping clustering algorithm based on graph theory
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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In this paper we present a new algorithm for document clustering called Generalized Star (GStar). This algorithm is a generalization of the Star algorithm proposed by Aslam et al., and recently improved by them and other researchers. In this method we introduced a new concept of star allowing a different star-shaped form with better overlapping clusters. The evaluation experiments on standard document collections show that the proposed algorithm outperforms previously defined methods and obtains a smaller number of clusters. Since the GStar algorithm is relatively simple to implement and is also efficient, we advocate its use for tasks that require clustering, such as information organization, browsing, topic tracking, and new topic detection.