Data Mining And Knowledge Discovery With Evolutionary Algorithms
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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.
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
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.
Data Mining And Knowledge Discovery With Evolutionary Algorithms
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Author : Freitas Alex A.
language : en
Publisher:
Release Date : 2007-10-01
Data Mining And Knowledge Discovery With Evolutionary Algorithms written by Freitas Alex A. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-01 with categories.
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.
Mathematical Methods For Knowledge Discovery And Data Mining
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Author : Giovanni Felici
language : en
Publisher: IGI Global Snippet
Release Date : 2008
Mathematical Methods For Knowledge Discovery And Data Mining written by Giovanni Felici and has been published by IGI Global Snippet this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Annotation The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.
Soft Computing For Knowledge Discovery And Data Mining
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Author : Oded Maimon
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-25
Soft Computing For Knowledge Discovery And Data Mining written by Oded Maimon 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 2007-10-25 with Computers categories.
Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining. This edited volume by highly regarded authors, includes several contributors of the 2005, Data Mining and Knowledge Discovery Handbook. This book was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including manufacturing, medical, banking, insurance and others, but also includes various real-world case studies with detailed results. Soft Computing for Knowledge Discovery and Data Mining is designed for practitioners and researchers in industry. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a secondary textbook or reference for advanced-level students in information systems, engineering, computer science and statistics management.
Multi Objective Evolutionary Algorithms For Knowledge Discovery From Databases
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Author : Ashish Ghosh
language : en
Publisher: Springer
Release Date : 2008-02-28
Multi Objective Evolutionary Algorithms For Knowledge Discovery From Databases 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 2008-02-28 with Technology & Engineering categories.
The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.
Data Preprocessing In Data Mining
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Author : Salvador García
language : en
Publisher: Springer
Release Date : 2014-08-30
Data Preprocessing In Data Mining written by Salvador García and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-30 with Technology & Engineering categories.
Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.
Fuzzy Modeling And Genetic Algorithms For Data Mining And Exploration
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Author : Earl Cox
language : en
Publisher: Academic Press
Release Date : 2005-02
Fuzzy Modeling And Genetic Algorithms For Data Mining And Exploration written by Earl Cox and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02 with Computers categories.
Foundations and ideas -- Principal model types -- Approaches to model building -- Fundamental concepts of fuzzy logic -- Fundamental concepts of fuzzy systems -- Fuzzy SQL and intelligent queries -- Fuzzy clustering -- Fuzzy rule induction -- Fundamental concepts of genetic algorithms -- Genetic resource scheduling optimization -- Genetic tuning of fuzzy models.