Abstract
We investigate the impact of an initialization strategy on the quality of fuzzy-based clustering, applied to creation of maps of text document collection. In particular, we study the effectiveness of bootstrapping as compared to traditional “randomized” initialization. We show that the idea is effective both for traditional Fuzzy K-Means algorithm and for a new one, applying histogram-based cluster description.
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Ciesielski, K., Kłopotek, M.A., Wierzchoń, S.T. (2008). Term Distribution-Based Initialization of Fuzzy Text Clustering. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_31
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DOI: https://doi.org/10.1007/978-3-540-68123-6_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68122-9
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