APPLYING PARALLEL ASSOCIATION RULE MINING TO HETEROGENEOUS ENVIRONMENT

The work aims to discover frequent patterns by generating the candidates and frame the association rules after which filter out only the efficient rules based on various Rule Interestingness measures. As all these require heavy computation, application of complete parallelization to every individual phase would yield better performance. The paper illustrates the system behavior in a heterogeneous environment with both shared memory and distributed memory parallelization while efficiently mining the data.