HIGH PERFORMANCE DATA MINING IN DISTRIBUTED DATABASES

Abstract

The massive volume of data generated on daily basis decreases the ability of current data mining techniques to generate knowledge in a short time. The constant change in data requires constant updating of the existing patterns. It is computationally intensive to repeat the knowledge discovery process on the whole databases with every update. Therefore, there is a need to enhance the performance of association rules mining methodologies when dealing with incremental updates. In order to enhance the performance of incremental association rules mining, this thesis focus on the utilization of current hardware and software advances in high-performance computing. This thesis proposes a distributed incremental association rules mining approach based on MPI. In addition, the thesis also proposes a hybrid incremental mining