Cluster Based Text Mining |
Author(s): |
| Nikita Rajan Andhrutkar , MET's Institute of Engineering, Nashik; Prashant M. Yawalkar, MET's Institute of Engineering, Nashik |
Keywords: |
| Text Mining, Text Feature Extraction, Text Classification |
Abstract |
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In text mining, discovery of relevance features (RFD) is the challenging task as term-based approach is used for it. A term based approach suffered from the problems of polysemy and synonymy. Pattern based method is also introduced in previous systems which perform better than term based approach. In proposed system, relevance feature discovery model is introduced which discovers positive and negative patterns from given dataset of text documents. Pattern taxonomy mining i.e. PTM along with n-gram is proposed for pattern discovery. The proposed model describes relevance feature in three groups such as, positive, negative and general. F-Clustering algorithm describes the feature clustering approach in which set of positive documents DP+ and set of negative documents DP- get sorted. Term based classification is given as a contribution of proposed work. |
Other Details |
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Paper ID: IJSRDV5I50336 Published in: Volume : 5, Issue : 5 Publication Date: 01/08/2017 Page(s): 483-488 |
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