Alex A. Freitas's Books

Data Mining and Knowledge Discovery with Evolutionary Algorithms

By: Alex A. Freitas

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Data Mining and Knowledge Discovery with Evolutionary Algorithms

By: Alex A. Freitas

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics

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272

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Springer Science & Business Media

Published Date

ISBN 10

3662049236

ISBN 13

9783662049235

Automatic Design of Decision-Tree Induction Algorithms

By: Rodrigo C. Barros,André C.P.L.F de Carvalho,Alex A. Freitas

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Automatic Design of Decision-Tree Induction Algorithms

By: Rodrigo C. Barros,André C.P.L.F de Carvalho,Alex A. Freitas

Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.

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184

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Springer

Published Date

ISBN 10

3319142313

ISBN 13

9783319142319

Mining Very Large Databases with Parallel Processing

By: Alex A. Freitas,Simon H. Lavington

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Mining Very Large Databases with Parallel Processing

By: Alex A. Freitas,Simon H. Lavington

Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.

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211

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Springer Science & Business Media

Published Date

ISBN 10

1461555213

ISBN 13

9781461555216

Automating the Design of Data Mining Algorithms

An Evolutionary Computation Approach

By: Gisele L. Pappa,Alex Freitas

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Automating the Design of Data Mining Algorithms

An Evolutionary Computation Approach

By: Gisele L. Pappa,Alex Freitas

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.

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Page Count

198

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3642025412

ISBN 13

9783642025419

Book's by Alex A. Freitas

Data Mining and Knowledge Discovery with Evolutionary Algorithms

Data Mining and Knowledge Discovery with Evolutionary Algorithms

Automatic Design of Decision-Tree Induction Algorithms

Automatic Design of Decision-Tree Induction Algorithms

Mining Very Large Databases with Parallel Processing

Mining Very Large Databases with Parallel Processing

Automating the Design of Data Mining Algorithms

Automating the Design of Data Mining Algorithms

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