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oai:www.ideals.illinois.edu:2142/81763 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Snir, Marc Jiang, Changhao 2015-09-25T20:20:22Z 2015-09-25T20:20:22Z 10000-01-01 2007 2007 Frequent pattern mining is a fundamental problem in data mining and a large number of distinct algorithms have been proposed to solve it efficiently. However, no single algorithm outperforms all the others since their relative performance highly depends on the characteristics of the input data. In the dissertation, we present a machine learning based approach to select the best frequent pattern mining algorithm based on the input characteristics. Three of the fastest publicly available algorithms, FP_Growth, LCM and Eclat, were extensively evaluated using synthetic data sets. The results of these evaluations were used to train a support-vector machine (SVM) prediction system, which is then used at runtime to predict the best mining algorithm for real-world data sets. Our experiments show that the runtime prediction overhead is negligible and that the trained SVM prediction system usually identifies the best algorithm. In case of misprediction, the selected algorithm is still competitive in performance. Made available in DSpace on 2015-09-25T20:20:22Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3269927.pdf: 2833916 bytes, checksum: d59f99ce84512a6124ac4352f3785cf8 (MD5) Previous issue date: 2007 Embargo set by: Seth Robbins for item 83044 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 87 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. http://hdl.handle.net/2142/81763 (MiAaPQ)AAI3269927 eng Computer Science Automatic Software Performance Optimization on Modern Architectures text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.