Apriori Algorithm Essay
SURVEY PAPER ON POSITIVE ASSOCIATION RULE MINING
L. Ravi kiran Goud Dr. Y. Rama Devi
SECOND YEAR MTECH CSE Head of the CSE Dept
CBIT HYDERABAD CBIT HYDERABAD ABSTRACT
Data mining is a technique of extracting knowledge from Enormous amount of data.To generate frequent item sets Apriori Algorithm is used. From these frequent item sets we generate rules. Rules which satisfy minimum confidence are called as Association rules. Association Rules can be either positive or negative. Along with the positive association rules negative association rules also helps to analyze the consumer buying habits .In the traditional Apriori Algorithm, in every iteration we are ignoring some items as they fail to satisfy minimum support level. All these items which are ignored by us constitute in frequent item set from which we derive negative association rules. Alternate mechanism to generate frequent item set is FP tree(Frequent Pattern Tree).We even have mechanisms like Positive Negative Association Rule (PNAR) & Interesting Multiple Level Minimum Supports (IMLMS).A new model is supposed to be developed which is a combination of both PNAR & IMLMS known as PNAR_IMLMS
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