ABSTRACT

Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. Mining frequent patterns from large scale databases has emerged as an important problem in data mining and knowledge discovery community. A number of algorithms has been proposed to determine frequent pattern. Apriori algorithm is the first algorithm proposed in this field. With the time a number of changes proposed in Apriori to enhance the performance in term of time and number of database passes. In this paper three different frequent pattern mining approaches (Record filter, Intersection and Proposed Algorithm) are given based on classical Apriori algorithm. In these approaches Record filter approach proved better than classical Apriori Algorithm, Intersection approach proved better than Record filter approach and finally proposed algorithm proved that it is much better than other frequent pattern mining algorithm. In last we perform a comparative study of all approaches on dataset of 2000 transaction.