Generating Membership Values And Fuzzy Association Rules From Numerical Data
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 8
Abstract
The most important task in the design of fuzzy classification systems is to find a set of fuzzy rules from training data to deal with a specific classification problem. In this paper, a method to generate fuzzy rules from training data to deal with the data classification problem is presented. Partition method of interval is adopted in current classification based on associations (CBA). But this method cannot reflect the actual distribution of data and there exists the problem of sharp boundary. These type of problems can be approached with fuzzy representation of data. In this paper quantitative attributes are partitioned into several fuzzy sets by fuzzy C-Means algorithm and embership values are generated, and supervised association rule lgorithm is used to discover interesting fuzzy association rules, which are used to build classification system. In this paper fuzzy classified association rules are generated and three lassifiers namely C4.5, Naïvebayes , and ID3 are used for lassification. Experiments are conducted on both primary and secondary data and accuracy of each of the classifiers are iscussed with AUC-ROC curves. Quantitative values in databases generate very large number of rules. Using fuzzy linguistic values the generation of rules can be reduced and an objective measure is used further to filter the generated rules and present only the interesting rules.
Authors and Affiliations
Dr. R. Radha , Dr. S. P. Rajagopalan
Keywords
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