Association rule mining is the most popular data mining techniques
to find association among items in a set by mining necessary
patterns in a large database, frequently used in marketing,
advertising and inventory control. Typically association rules
consider only items enumerated in transactions, referred as
positive association rules but not consider negative occurrence of
attributes that are also useful in market-basket analysis to
identify products that conflict with each other or products that
complement each other. Also for mining those positive rules that
qualify the user specified threshold criteria, algorithm generates
too many candidate itemsets by scanning database multiple times. In
order to resolve all the bottleneck of association rule mining
algorithm, in this we propose an algorithm SARIC which implements
Set Particle Swarm Optimization heuristic technique for generating
association rules from a database that also consider negative
occurrence of attribute along with positive occurrence. SARIC uses
the concept of IR and Correlation Coefficient and there is no need
to specify minimum support and confidence, it automatically
determines them quickly and objectively
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