Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Natural Computing Series)

Pappa, Gisele L.; Freitas, Alex

ISBN 10: 3642261256 / ISBN 13: 9783642261251

Edité par Springer, 2012

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N° de réf. du vendeur ria9783642261251_new

Détails bibliographiques

• Titre: Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Natural Computing Series)
• Éditeur: Springer
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 3642261256
• ISBN à 13 chiffres: 9783642261251
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.