Abstract
In this paper, we propose a method to detect hot event automatically. We use all the web pages from Jan 1st 2005 to Dec 31st 2005, and detect new events by using incremental TF-IDF model and incremental cluster algorithm. Based on analysis of the attributes of events, we propose a method to measure the activity of events, then filter and sort the event according to the activity of events; finally a hot event list can be derived.
The paper is supported by National Natural Science Foundation of China (NSFC), (Grant No.60496323; Grant No.60375016 Grant No.10071028;); Ministry of education of China, Research Project for Science and technology, (Grant No. 105117).
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He, T., Qu, G., Li, S., Tu, X., Zhang, Y., Ren, H. (2006). Semi-automatic Hot Event Detection. 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_110
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DOI: https://doi.org/10.1007/11811305_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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