Rule Pruning with Correctly Classify in Associative Classification |
Author(s): |
| Jogendraprasad Bagdi , Kalol Institute Of Technology and Research, Kalol. |
Keywords: |
| data mining, Rule Pruning, Associative Classification |
Abstract |
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Recent studies in data mining revealed that Associative Classification (AC) data mining approach builds competitive classification classifiers with reference to accuracy when compared to classic classification approaches including decision tree and rule based. Nevertheless, AC algorithms suffer from a number of known defects as the generation of large number of rules which makes it hard for end-user to maintain and understand its outcome and the possible over-fitting issue caused by the confidence-based rule evaluation used by AC. This thesis attempts to deal with reduces the number of generated rules without having large impact on the prediction rate of the classifiers. In this paper proposed method FPCC (Full And Partial Rule Coverage Correctly Classifier) is used to discover the rules which are fully matched and partially match with correct classifier. |
Other Details |
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Paper ID: IJSRDV2I10004 Published in: Volume : 2, Issue : 10 Publication Date: 01/01/2015 Page(s): 1-3 |
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