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- W2029568075 abstract "Document clustering has been investigated for use in different areas of text mining and information retrieval. A clustering depends on the chosen clustering algorithm as well as on the algorithm's parameter settings; for that reason it is necessary to find the best among several clustering techniques. However, it is very difficult to evaluate a given clustering of documents. There are external, internal and relative measures. The disadvantage of external measures is the necessity of a human reference classification to evaluate the clustering. In this paper we propose the use of rough-set-based measures for document clustering evaluation, basing our calculations solely on the clustering that has to be evaluated. Thus, two advantages of rough set theory are used: it does not need any preliminary or additional information about data, and it is a tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty (this is the case of document clustering). We illustrate the use of the novel measures." @default.
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- W2029568075 date "2006-11-01" @default.
- W2029568075 modified "2023-10-15" @default.
- W2029568075 title "On Clustering Validity Measures and the Rough Set Theory" @default.
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- W2029568075 doi "https://doi.org/10.1109/micai.2006.36" @default.
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