Using distributed apriori association rule and classical apriori mining algorithms for grid based knowledge discovery

The aim of this paper is to extract knowledge using predictive apriori and distributed grid based apriori algorithms for association rule mining. The paper presents the implementation of an association rules discovery data mining task using Grid technologies. A result of implementation with a comparison of classic apriori and distributed apriori is also discussed. Distributed data mining systems provide an efficient use of multiple processors and databases to speed up the execution of data mining and enable data distribution. To evaluate the efficiency of the proposed system, performance analysis of apriori and predictive apriori algorithms on a centralized database have been given using weka tool. The main aim of grid computing is to give organizations and application developers the ability to create distributed computing environments that can utilize computing resources on demand. Therefore, it can help increase efficiencies and reduce the cost of computing networks by decreasing data processing time and optimizing resources and distributing workloads, thereby allowing users to achieve much faster results on large operations and at lower costs. In this paper distributed apriori association rule on grid based environment is mined and the knowledge obtained is interpreted.