
Automating the Design of Data Mining Algorithms by Gisele L Pappa
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.-
Robot Evolution
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An Introduction to Metaheuristics for Optimization
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Deep Neural Evolution
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Lectures on Intelligent Systems
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
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Coevolutionary Computation and Its Applications
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Visions of DNA Nanotechnology at 40 for the Next 40
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Cartesian Genetic Programming
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Bioinspired Computation in Combinatorial Optimization
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Self-organising Software
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Swarm Intelligence
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Evolution as Computation
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Algorithmic Bioprocesses
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General-Purpose Optimization Through Information Maximization
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Nature Inspired Optimisation for Delivery Problems
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Experimental Research in Evolutionary Computation
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Hyper-Heuristics: Theory and Applications
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Modelling in Molecular Biology
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Advances in Metaheuristics for Hard Optimization
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Foraging-Inspired Optimisation Algorithms
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Mobility in Process Calculi and Natural Computing
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Contemporary Evolution Strategies
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Theory and Principled Methods for the Design of Metaheuristics
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Discrete and Topological Models in Molecular Biology
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Massively Parallel Evolutionary Computation on GPGPUs
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Multimodal Optimization by Means of Evolutionary Algorithms
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Computation in Living Cells
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Sensitivity Analysis for Neural Networks
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Reservoir Computing
From the reviews:
"The book is targeted at researchers and postgraduate studentsAs 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)
| SKU | Unavailable |
| ISBN 13 | 9783642261251 |
| ISBN 10 | 3642261256 |
| Title | Automating the Design of Data Mining Algorithms |
| Author | Gisele L Pappa |
| Series | Natural Computing Series |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer |
| Year published | 2012-03-14 |
| Number of pages | 187 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |




























