Comparative Analysis of Association Rule Mining Algorithms for the Distributed Data

Abstract

Many current data mining tasks can be accomplished successfully only in a distributed setting. The field of distributed data mining has therefore gained increasing importance in the last decade. The Apriori algorithm by Rakesh Agarwal has emerged as one of the best Association Rule mining algorithms. FP Growth also serves as the base algorithm for most parallel algorithms. The enormity and high dimensionality of datasets typically available as input to problem of association rule discovery, makes it an ideal problem for solving on multiple processors in parallel. The primary reasons are the memory and CPU speed limitations faced by single processors. In this paper an Association Rule mining algorithms for geographically distributed data is used in parallel and distributed environment so that it reduces communication costs. The response time is calculated in this environment using Supermarket data.