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Pattern discovery and model construction: an evolutionary learning and data mining approach

Zhou, Harry · SEDICI UNLP · 2006

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In the information age, knowledge leads to profits, power and success. As an ancestor of data mining, machine learning has concerned itself with discovery of new knowledge on its own. This paper presents experiment results produced by genetic algorithms in the domains of model construction and event predictions, the areas where data mining systems have been focusing on. The experiment results have shown that genetic algorithms are able to discover useful patterns and regularities in large sets of data, and to construct models that conceptualize input data. It demonstrates that genetic algorithms are a powerful and useful learning algorithm for solving fundamental tasks data mining systems are facing today. Applications in Artificial Intelligence - Knowledge Discovery Red de Universidades con Carreras en Informática (RedUNCI)

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APA 7

Zhou, H. (2006). Pattern discovery and model construction: an evolutionary learning and data mining approach. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/24215

MLA

Zhou, Harry. Pattern discovery and model construction: an evolutionary learning and data mining approach. SEDICI UNLP, 2006. http://sedici.unlp.edu.ar/handle/10915/24215.

Chicago

Zhou, Harry. 2006. Pattern discovery and model construction: an evolutionary learning and data mining approach. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/24215.

Harvard

Zhou, H. 2006, Pattern discovery and model construction: an evolutionary learning and data mining approach, SEDICI UNLP, available at: http://sedici.unlp.edu.ar/handle/10915/24215 [Accessed 7 Aug. 2026].

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Title
Pattern discovery and model construction: an evolutionary learning and data mining approach
Author / contributors
Zhou, Harry
Publisher
SEDICI UNLP
Publication year
2006
Language
English

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