Evaluating the Performance of Proposed IODAM Algorithm

Abstract

Distributed Association Rule Mining (DARM) is a technique used to identify the desired buying patterns of the products bought by the customers from the transaction set that are distributed in various nodes located geographically at different locations. The paper describes the performance of proposed DARM algorithm called IODAM (Incremental Optimized Distributed Association Mining) using the various experiments conducted for calculating execution time on given datasets in a distributed environment. Scalability refers to the evaluating the performance of the algorithm when the nodes are added in the distributed environment. Scalability testing for factors such as Scaleup and Speedup is performed and measured for the IODAM algorithm, and the algorithm withstands all the factors efficiently.