Multiobjective Genetic Algorithms For Clustering
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Multiobjective Genetic Algorithms For Clustering
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Author : Ujjwal Maulik
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-09-01
Multiobjective Genetic Algorithms For Clustering written by Ujjwal Maulik 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 2011-09-01 with Computers categories.
This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries. The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.
Multiobjective Genetic Algorithms For Clustering
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Author :
language : en
Publisher: Springer
Release Date : 2011-09-02
Multiobjective Genetic Algorithms For Clustering written by and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-02 with categories.
Evolutionary Data Clustering Algorithms And Applications
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Author : Ibrahim Aljarah
language : en
Publisher: Springer Nature
Release Date : 2021-02-20
Evolutionary Data Clustering Algorithms And Applications written by Ibrahim Aljarah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-20 with Technology & Engineering categories.
This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
Multi Objective Evolutionary Algorithms For Knowledge Discovery From Databases
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Author : Ashish Ghosh
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-19
Multi Objective Evolutionary Algorithms For Knowledge Discovery From Databases 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 2008-03-19 with Mathematics 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.
Multi Objective Optimization
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Author : Jyotsna K. Mandal
language : en
Publisher: Springer
Release Date : 2018-08-18
Multi Objective Optimization written by Jyotsna K. Mandal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-18 with Computers categories.
This book brings together the latest findings on efficient solutions of multi/many-objective optimization problems from the leading researchers in the field. The focus is on solving real-world optimization problems using strategies ranging from evolutionary to hybrid frameworks, and involving various computation platforms. The topics covered include solution frameworks using evolutionary to hybrid models in application areas like Analytics, Cancer Research, Traffic Management, Networks and Communications, E-Governance, Quantum Technology, Image Processing, etc. As such, the book offers a valuable resource for all postgraduate students and researchers interested in exploring solution frameworks for multi/many-objective optimization problems.
Metaheuristic Clustering
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Author : Swagatam Das
language : en
Publisher: Springer
Release Date : 2009-01-30
Metaheuristic Clustering written by Swagatam Das and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-30 with Computers categories.
Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.
Evolutionary Multi Criterion Optimization
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Author : Carlos A. Coello Coello
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-02-17
Evolutionary Multi Criterion Optimization written by Carlos A. Coello Coello 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 2005-02-17 with Computers categories.
This book constitutes the refereed proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization, EMO 2005, held in Guanajuato, Mexico, in March 2005. The 59 revised full papers presented together with 2 invited papers and the summary of a tutorial were carefully reviewed and selected from the 115 papers submitted. The papers are organized in topical sections on algorithm improvements, incorporation of preferences, performance analysis and comparison, uncertainty and noise, alternative methods, and applications in a broad variety of fields.
Multiobjective Problem Solving From Nature
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Author : Joshua Knowles
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-01-28
Multiobjective Problem Solving From Nature written by Joshua Knowles 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 2008-01-28 with Computers categories.
This text examines how multiobjective evolutionary algorithms and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concept of multiobjective optimization can be used to reformulate and resolve problems in areas such as constrained optimization, co-evolution, classification, inverse modeling, and design.
Metaheuristics For Big Data
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Author : Clarisse Dhaenens
language : en
Publisher: John Wiley & Sons
Release Date : 2016-08-16
Metaheuristics For Big Data written by Clarisse Dhaenens and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-16 with Computers categories.
Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.
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.