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Title:Automatic Software Performance Optimization on Modern Architectures
Author(s):Jiang, Changhao
Doctoral Committee Chair(s):Snir, Marc
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
Abstract: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.
Issue Date:2007
Description:87 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
Other Identifier(s):(MiAaPQ)AAI3269927
Date Available in IDEALS:2015-09-25
Date Deposited:2007

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