Horizantal DistributionAssociation Rule Mining

Association rule mining (ARM) has become one of the core data mining tasks. ARM is an undirected or unsupervised data mining technique which works on variable length data, and produces clear and understandable results. Association rule mining is an active data mining research area.ARM algorithms provides to a centralized environment. The aim of Association Rule Mining is to detect relationships or patterns between specific values of categorical variables in large data sets. The main idea of association rule mining in the existing algorithm is to partition the attribute values into Transaction patterns. Basically, this technique enables analysts and researchers to uncover hidden patterns in large data sets. Here the pre-processed data is stored in such a way that online rule generation may be done with a complexity proportional to the size of the output. An optimized algorithm for online rule generation is used in the existing model with adjacency lattice. This algorithm generates all the essential rules and no rule is missing. With the motivation gained from the online rule generation model here we propose a novel Horizontal distributed Association rule mining algorithm for Parallel distributed datasets. ODAM is a distributed algorithm for geographically distributed data sets that reduces communication costs. This algorithm aims to generate rules from different data sets spread over various geographical sites, hence, they require external communications throughout the entire process.