Association Rule mining using Apriori Algorithm: A Review

Manisha Bhargava, Arvind Selwal


Data mining or knowledge discovery is the process of discovering patterns in large data sets. In data mining each algorithm has a
different objective and to obtain meaningful and previously unknown patterns from large dataset is an emerging and challenging problem.
Association rule mining is a technique for discovering unsuspected data dependencies and is one of the best known data mining techniques. The
basic Idea to identify from a given database, consisting of item sets (e.g. shopping baskets), whether the occurrence of specific items, implies
also the occurrence of other items with a relatively high probability. Apriori algorithm is one of the popular approaches which are used to extract
association rules from data sets. One of the most popular data mining approaches is to find frequent item sets from a transaction dataset and
derive association rules. In this paper, we describe the association rules which are descriptive data mining technique. This paper also addresses
Apriori Algorithm and two other algorithms Record filter and Intersection Approach based on Apriori.

Keywords: Data Mining, Association rules, Apriori Algorithm, Record filter approach, Intersection Approach.

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