EXPERIMENTAL EVALUATION OF APRIORI AND EQUIVALENCE CLASS CLUSTERING AND BOTTOM UP LATTICE TRAVERSAL (ECLAT) ALGORITHMS

Abstract

Frequent pattern mining is the beginning of association rule mining. Association rule mining is the strongly scrutinized techniques in data mining. The basic algorithms of Apriori and ECLAT are the most identified algorithms for mining frequent patterns in association rule mining. This paper describes the application of these two algorithms that use many to achieve maximum efficiency with regards to turnaround time and memory capacity. Both algorithms are executed using discrete data sets and are further analyzed based on their performances. The performance analysis is based on different parameters such as support, speedup etc., with different quantities of datasets.