Abstract
Text clustering is one of the most important research areas in text mining, which handles the text automatically to discover implicit knowledge. It groups text into different clusters by contents without apriori knowledge. In this paper, different text clustering methods are studied and three text clustering validation criteria are studied and used to evaluate the experimental results. We compare and contrast the effectiveness of k-means and FIHC text clustering methods by experiments, and address the different levels of quality of the resulting text clusters.
The research is funded by National Natural Science Foundation of China (Project No. 60402011) and Ministry of Education Key Laboratory of Information Management and Information Economics (Project No. F0607-01).
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Zheng, Y., Cheng, X., Huang, R., Man, Y. (2006). A Comparative Study on Text Clustering Methods. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_71
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DOI: https://doi.org/10.1007/11811305_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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