A Novel Parallel Algorithm for Frequent Itemset Mining of Incremental Dataset

Most Algorithms for frequent item set mining typically make the assumption that data is centralized or static. They may waste computational and I/O resources when the data is dynamic, and they impose excessive communication overhead when the data is distributed. As a result, the data mining process is harmed by slow response time. In this paper we propose a novel algorithm that uses overlapping data partitions and parallelizes the workload among machines efficiently. Experiments confirm that our algorithm results in excellent running time improvements.