TITLE:
Correlation Associative Rule Induction Algorithm Using ACO
AUTHORS:
C. Nalini
KEYWORDS:
Associative Classification, Mixed Data, Correlation, ACO, Mixed-Kernel PDF
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.10,
August
4,
2016
ABSTRACT: Classification and
association rule mining are used to take decisions based on relationships
between attributes and help decision makers to take correct decisions at right
time. Associative classification first generates class based association rules
and use that generate rule set which is used to predict the class label for
unseen data. The large data sets may have many null-transac- tions. A null-transaction is a transaction that
does not contain any of the itemsets being examined. It is important to
consider the null invariance property when selecting appropriate interesting
measures in the correlation analysis. Real time data set has mixed attributes.
Analyze the mixed attribute data set is not easy. Hence, the proposed work uses
cosine measure to avoid the influence of null transactions during rule
generation. It employs mixed-kernel probability density function (PDF) to
handle continuous attributes during data analysis. It has ably to handle both
nominal and continuous attributes and generates mixed attribute rule set. To
explore the search space efficiently it applies Ant Colony Optimization (ACO).
The public data sets are used to analyze
the performance of the algorithm. The results illustrate that the
support-confidence framework with
a correlation measure generates more accurate simple rule set and discover more
interesting rules.