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ABSTRACT
Title |
: |
Generating Membership Values And Fuzzy Association Rules From Numerical Data |
Authors |
: |
Dr.R.Radha, Dr.S.P.Rajagopalan |
Keywords |
: |
classification, quantitative attributes, Naivebayes,
C4.5, ID3, fuzzy C-Means, fuzzy association rules, Supervised assoc
rule |
Issue Date |
: |
November 2010 |
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 membership values are
generated, and supervised association rule algorithm 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 classifiers namely C4.5, Naïvebayes , and
ID3 are used for classification. Experiments are conducted on both
primary and secondary data and accuracy of each of the classifiers
are discussed 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. |
Page(s) |
: |
2705-2715 |
ISSN |
: |
0975–3397 |
Source |
: |
Vol. 2, Issue.8 |
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