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Abstract: TopCat (Topic Categories) is a technique for identifying topics that recur in articles in a text corpus. Natural language processing techniques are used to identify key entities in individual articles, allowing us to represent an article as a set of items. This allows us to view the problem in a database/data mining context: Identifying related groups of items. This paper presents a novel method for identifying related items based on "traditional" data mining techniques. Frequent itemsets are... (Update)
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BibTeX entry: (Update)
Clifton, C. and Cooley, R., TopCat: data mining for topic identification in a text corpus. In Proceedings of the 3rd European Conference of Principles and Practice of Knowledge Discovery in Databases, Prague, Czech Republic, 1999. http://citeseer.ist.psu.edu/article/clifton02topcat.html More
@inproceedings{ clifton99topcat,
author = "Chris Clifton and Robert Cooley",
title = "TopCat: Data Mining for Topic Identification in a Text Corpus",
booktitle = "Principles of Data Mining and Knowledge Discovery",
pages = "174-183",
year = "1999",
url = "citeseer.ist.psu.edu/article/clifton02topcat.html" }
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