Parallel Single and Multi-Objective Genetic Algorithm in Datamining » książka
Parallel Single and Multi-Objective Genetic Algorithm in Datamining
ISBN-13: 9783659255212 / Angielski / Miękka / 2014 / 268 str.
Genetic algorithms (GAs) is a powerful stochastic search techniques that is used successfully to solve problems in various disciplines like business, engineering, and science, with various degrees of complexity. In many complex optimization / search problems, (i.e., specifically intractable and multi-modal case) GAs can obtain optimal solution in tolerable time. Most of the problems of Knowledge Discovery in Database (KDD) and Data mining (DM) are NP-Complete, trapped by multiple local optima, and often multi-objective in nature. Therefore, GAs can be choosen as a suitable tool to solve the aforesaid problems. However, the tasks like Association Rule Mining (ARM), Classification and Associative Classification Rule Mining (ACRM) needs multiple scan of the database to obtain a set of optimal rules which hinders the user for using GAs. Hence, this book focuses on the parallelization of GAs to solve tasks like feature section, association rule mining, classification, and associative classification.
Genetic algorithms (GAs) is a powerful stochastic search techniques that is used successfully to solve problems in various disciplines like business, engineering, and science, with various degrees of complexity. In many complex optimization / search problems, (i.e.,specifically intractable and multi-modal case) GAs can obtain optimal solution in tolerable time. Most of the problems of Knowledge Discovery in Database (KDD) and Data mining (DM) are NP-Complete, trapped by multiple local optima, and often multi-objective in nature. Therefore, GAs can be choosen as a suitable tool to solve the aforesaid problems. However, the tasks like Association Rule Mining (ARM), Classification and Associative Classification Rule Mining (ACRM) needs multiple scan of the database to obtain a set of optimal rules which hinders the user for using GAs. Hence, this book focuses on the parallelization of GAs to solve tasks like feature section, association rule mining, classification, and associative classification.
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