Performance Evaluation of Algorithms using a Distributed Data Mining Frame Work based on Association Rule Mining

Numerous current data mining tasks can be implemented effectively only in a distributed data mining. Thus distributed data mining has achieved significant importance in the last decade. The proposed distributed data mining application framework, is a data mining tool. This framework aims at developing an efficient association rule mining tool to support effective decision making. Association Rule mining focuses on finding interesting patterns from huge amount of data available in the data warehouses. In order to build strong association rules, it depends on the extraction of association rules by Apriori algorithm, AprioriTID algorithm, AprioriHyprid algorithm, FP growth etc. The efficiency of the distributed data mining framework is determined based on the selection of the algorithm. The object oriented implementation has enabled the system to be platform independent. The use of self defined database format gives an upper hand for the system by operating efficiently without any need for third party database drivers. The mined results can be compared and graphically projected. Finally, some expectations for future work are presented where various modes of graphical representations can be included. Keywords-Association Rule Mining, Frequent Intemset, Frequent Pattern Mining, Distributed Data Mining