Feature Selection In Document Clustering Using Rough Set Theory | ||||
Journal of the ACS Advances in Computer Science | ||||
Article 4, Volume 1, Issue 1, 2007, Page 39-49 PDF (745.13 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/asc.2007.147560 | ||||
View on SCiNiTO | ||||
Abstract | ||||
One fundamental aspect of rough set theory is the search of subsets of attributes that provide the same information for classification purposes as the full set of attributes. In this paper, application of rough set theory to feature selection in document clustering is introduced. We emphasize the role of the basic constructs of rough set approach in feature selection, namely reducts. We propose a method of generating a best reduct of the data based on rough set theory to overcome the problems of generating all reducts. The application to a hierarchical clustering of document dataset is presented as an example. Finally, the paper presents a comparison of the clustering results based on the original data set and those based on the reduced data set. | ||||
Keywords | ||||
Rough set theory; feature selection; feature extraction; document clustering; and data reduction | ||||
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