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Evolutionary Computation In Data Mining


Evolutionary Computation In Data Mining
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Data Mining And Knowledge Discovery With Evolutionary Algorithms


Data Mining And Knowledge Discovery With Evolutionary Algorithms
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Author : Alex A. Freitas
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11

Data Mining And Knowledge Discovery With Evolutionary Algorithms written by Alex A. Freitas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Computers categories.


This book addresses the integration of two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increas ingly popular in the last few years, and their integration is currently an area of active research. In essence, data mining consists of extracting valid, comprehensible, and in teresting knowledge from data. Data mining is actually an interdisciplinary field, since there are many kinds of methods that can be used to extract knowledge from data. Arguably, data mining mainly uses methods from machine learning (a branch of artificial intelligence) and statistics (including statistical pattern recog nition). Our discussion of data mining and evolutionary algorithms is primarily based on machine learning concepts and principles. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which the user can potentially use to make intelligent decisions. In a nutshell, 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 (rules or another form of knowl edge representation). In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.



Evolutionary Computation In Data Mining


Evolutionary Computation In Data Mining
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Author : Ashish Ghosh
language : en
Publisher: Springer
Release Date : 2006-06-22

Evolutionary Computation In Data Mining written by Ashish Ghosh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-22 with Computers categories.


Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).



Data Mining With Computational Intelligence


Data Mining With Computational Intelligence
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Author : Lipo Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-08

Data Mining With Computational Intelligence written by Lipo Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-08 with Computers categories.


Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.



Data Mining And Knowledge Discovery With Evolutionary Algorithms


Data Mining And Knowledge Discovery With Evolutionary Algorithms
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Author : Alex A. Freitas
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-08-21

Data Mining And Knowledge Discovery With Evolutionary Algorithms written by Alex A. Freitas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-08-21 with Computers categories.


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



Evolutionary Computation In Data Mining


Evolutionary Computation In Data Mining
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Author : Ashish Ghosh
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-10-18

Evolutionary Computation In Data Mining written by Ashish Ghosh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-10-18 with Computers categories.


Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).



Evolutionary Computation In Data Mining


Evolutionary Computation In Data Mining
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Author : Ashish Ghosh
language : en
Publisher: Springer
Release Date : 2009-09-02

Evolutionary Computation In Data Mining written by Ashish Ghosh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-02 with Computers categories.


Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).



Evolutionary Machine Learning Techniques


Evolutionary Machine Learning Techniques
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Author : Seyedali Mirjalili
language : en
Publisher: Springer Nature
Release Date : 2019-11-11

Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-11 with Technology & Engineering categories.


This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.



Computational Intelligence In Data Mining


Computational Intelligence In Data Mining
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Author : Himansu Sekhar Behera
language : en
Publisher: Springer
Release Date : 2019-08-17

Computational Intelligence In Data Mining written by Himansu Sekhar Behera and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-17 with Technology & Engineering categories.


This proceeding discuss the latest solutions, scientific findings and methods for solving intriguing problems in the fields of data mining, computational intelligence, big data analytics, and soft computing. This gathers outstanding papers from the fifth International Conference on “Computational Intelligence in Data Mining” (ICCIDM), and offer a “sneak preview” of the strengths and weaknesses of trending applications, together with exciting advances in computational intelligence, data mining, and related fields.



Advances In Evolutionary Computing


Advances In Evolutionary Computing
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Author : Ashish Ghosh
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-11-26

Advances In Evolutionary Computing written by Ashish Ghosh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-11-26 with Computers categories.


This book provides a collection of fourty articles containing new material on both theoretical aspects of Evolutionary Computing (EC), and demonstrating the usefulness/success of it for various kinds of large-scale real world problems. Around 23 articles deal with various theoretical aspects of EC and 17 articles demonstrate the success of EC methodologies. These articles are written by leading experts of the field from different countries all over the world.



Evolutionary Computation Machine Learning And Data Mining In Bioinformatics


Evolutionary Computation Machine Learning And Data Mining In Bioinformatics
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Author : Elena Marchiori
language : en
Publisher: Springer
Release Date : 2008-04-03

Evolutionary Computation Machine Learning And Data Mining In Bioinformatics written by Elena Marchiori and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-03 with Computers categories.


Coverage in this proceedings volume includes biomarker discovery, cell simulation and modeling, ecological modeling, gene networks, biotechnology, microarray analysis, protein interactions, proteomics, sequence analysis and alignment, and systems biology