Performance Evaluation of Distributed Association Rule Mining Algorithms

AbstractAssociation Rule Mining (ARM) is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Most ARM algorithms focus on a sequential or centralized environment where no external communication is required. Distributed ARM algorithms (DARM), aim to generate rules from different data sets spread over various geographical sites; hence, they require external communications throughout the entire process. DARM algorithm efficiency is highly dependent on data distribution. The Classical algorithms used in DARM are Count Distribution Algorithm (CDA), Fast Distributed Mining (FDM) Algorithm and Optimized Distributed Association Mining (ODAM) Algorithm. This paper presents the implementation details and experimental results of above mentioned algorithms. The paper also highlights the issues of message exchange size in a distributed environment of current DARM algorithms that can affect the communication costs in a distributed environment