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

Gisele L. Pappa (Author), Alex Freitas (Author)

Book Description

ISBN-10: 3642025404 | ISBN-13: 978-3642025402 | Publication Date: November 5, 2009 | Edition: 1

Traditionally, evolutionary computing techniques have been applied in the area of data mining either to optimize the parameters of data mining algorithms or to discover knowledge or patterns in the data, i.e., to directly solve the target data mining problem. This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters. The authors first offer introductory overviews on data mining, focusing on rule induction methods, and on evolutionary algorithms, focusing on genetic programming. They then examine the conventional use of evolutionary algorithms to discover classification rules or related types of knowledge structures in the data, before moving to the more ambitious objective of their research, the design of a new genetic programming system for automating the design of full rule induction algorithms. They analyze computational results from their automatically designed algorithms, which show that the machine-designed rule induction algorithms are competitive when compared with state-of-the-art human-designed algorithms. Finally the authors examine future research directions. This book will be useful for researchers and practitioners in the areas of data mining and evolutionary computation.

Editorial Reviews

Review

From the reviews: "The book is targeted at researchers and postgraduate students. As the amount of data being mined continues to grow it demands ever more sophisticated mining algorithms. Therefore there is a need for new algorithms and so Pappa and Freitas’ book will be of interest particularly to researchers in data mining. ... [T]his book will appeal to the target audience of [the journal] Genetic Programming and Evolvable Machines and, I feel, will align with the research interests of its readership." (John Woodward, Genetic Programming and Evolvable Machines (2011) 12:81–83) “The book will be useful for postgraduate students and researchers in the data mining field and in evolutionary computation.” (Florin Gorunescu, Zentralblatt MATH, Vol. 1183, 2010)

Product Details

• Hardcover: 200 pages
• Publisher: Springer; 1 edition (November 5, 2009)
• Language: English
• ISBN-10: 3642025404
• ISBN-13: 978-3642025402
• Product Dimensions: 9.4 x 6.8 x 0.7 inches
• Shipping Weight: 13.4 ounces (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #2,472,270 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

4.0 out of 5 stars Solid book on data mining via generative rule induction, December 4, 2009

By 

tangent (Houston, TX United States) - See all my reviews

This review is from: Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach (Natural Computing Series) (Hardcover)

This book covers data mining via automatic, evolutionary generation of rule induction algorithms using context based process grammar. It is relatively short at around 200 pages, the presence of equations and algorithms is relatively sparse but the ones which are included are useful and clearly presented.

The real strength of this book in my opinion is the very clear and straightforward way the authors explain the fundamental problems associated with building such a system, and the pros and cons of various methods for solving them (ie how to create an efficient yet accurate fitness function, considerations for designing a process grammar, avoiding overfitting etc).

I have read a number of other data mining books on similar subjects, many of them are quick to jump in to 'this is how you implement our method' without in depth explanation of the pros and cons of various aspects or implicit assumptions inherent in the technique. Either that or they approach the subject from an overly theoretical, mathematical point of view, focusing on proving the optimality of their technique in some limited scenario while ignoring the fact that most real world (high dimensional) datasets are too complex to be mined in this way. In contrast, I felt this book struck a good balance between theory and application.

Overall this is a good, solid introduction to data mining via generative rule induction. I would have given it 5 stars if it included a few more types of algorithms or made available sample code.

I would recommend it highly to beginners interested in this topic. Those who already have experience with similar techniques such as generative rule induction via neuro-fuzzy methods may or may not find it useful.