A Sampling-Based Framework for Parallel Mining Frequent Patterns

96 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.We implemented parallel algorithms for mining frequent itemsets, sequential patterns and closed-sequential patterns following our framework. A comprehensive performance study has been conducted in our experiments on both synthetic and real-world datasets. The experimental results have shown that our parallel algorithms have achieved good speedups on various datasets and the speedups are scalable up to 64 processors on our 64-processor system.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD