Optimization of Distributed Association Rule Mining Based Partial Vertical Partitioning

Association rule mining is a one of the most important technique in data mining. Data mining is the process of analyzing data from different angles & getting useful information about data. Modern organizations are geographically distributed. Using the traditional centralized association rule mining to discover useful patterns in such distributed system is not always feasible because merging data sets from different sites into a centralized site incurs huge network communication and time costs. This paper present an optimized Distributed Association Rule Mining (D-ARM) based on vertical partitioning. The existing D-ARM algorithms have lots of communication overhead, which is a major issue for concerning. The proposed approach minimizes this communication overhead and it is based on partial count. The papers then discuss the Partial Count on Vertical Dataset (TCDV) use of this structure which offers significant advantages with respect to existing DARM techniques.