Comparing the Performance of Frequent Pattern Mining Algorithms

Abstract

Frequent pattern mining is the widely researched field in data mining because of it's importance in many real life applications. Many algorithms are used to mine frequent patterns which gives different performance on different datasets. Apriori, Eclat and FP Growth are the initial basic algorithm used for frequent pattern mining. The premise of this paper is to find major issues/challenges related to algorithms used for frequent pattern mining with respect to transactional database.