Abstract
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.
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Suárez, A.P., Pagola, J.E.M. (2007). A Clustering Algorithm Based on Generalized Stars. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_19
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DOI: https://doi.org/10.1007/978-3-540-73499-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73498-7
Online ISBN: 978-3-540-73499-4
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