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2005
IEEE

Scrutinizing Frequent Pattern Discovery Performance

9 years 6 months ago
Scrutinizing Frequent Pattern Discovery Performance
Benchmarking technical solutions is as important as the solutions themselves. Yet many fields still lack any type of rigorous evaluation. Performance benchmarking has always been an important issue in databases and has played a significant role in the development, deployment and adoption of technologies. To help assessing the myriad algorithms for frequent itemset mining, we built an open framework and testbed to analytically study the performance of different algorithms and their implementations, and contrast their achievements given different data characteristics, different conditions, and different types of patterns to discover and their constraints. This facilitates reporting consistent and reproducible performance results using known conditions.
Mohammad El-Hajj, Osmar R. Zaïane, Stella Luk
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2005
Where ICDE
Authors Mohammad El-Hajj, Osmar R. Zaïane, Stella Luk, Yi Li
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