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
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
An incremental nested partition method for data clustering
Pattern Recognition
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
A new proposal for graph classification using frequent geometric subgraphs
Data & Knowledge Engineering
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In this paper we present a new algorithm for document clustering called Condensed Star (ACONS). This algorithm is a natural evolution of the Star algorithm proposed by Aslam et al., and improved by them and other researchers. In this method, we introduced a new concept of star allowing a different star-shaped form; in this way we retain the strengths of previous algorithms as well as address previous shortcomings. 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 ACONS 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.