Metaheuristic Clustering

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Metaheuristic Clustering

Swagatam Das,Ajith Abraham,Amit Konar
Metaheuristic Clustering Book PDF

Author : Swagatam Das,Ajith Abraham,Amit Konar
Publisher : Springer Science & Business Media
Page : 266 pages
File Size : 47,8 Mb
Release : 2009-03-24
Category : Computers
ISBN : 9783540921721

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Metaheuristic Clustering by Swagatam Das,Ajith Abraham,Amit Konar Pdf

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.

Recent Advances in Hybrid Metaheuristics for Data Clustering

Sourav De,Sandip Dey,Siddhartha Bhattacharyya
Recent Advances in Hybrid Metaheuristics for Data Clustering Book PDF

Author : Sourav De,Sandip Dey,Siddhartha Bhattacharyya
Publisher : John Wiley & Sons
Page : 196 pages
File Size : 52,7 Mb
Release : 2020-08-24
Category : Computers
ISBN : 9781119551591

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Recent Advances in Hybrid Metaheuristics for Data Clustering by Sourav De,Sandip Dey,Siddhartha Bhattacharyya Pdf

An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors—noted experts on the topic—provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.

Recent Advances in Hybrid Metaheuristics for Data Clustering

Sourav De,Sandip Dey,Siddhartha Bhattacharyya
Recent Advances in Hybrid Metaheuristics for Data Clustering Book PDF

Author : Sourav De,Sandip Dey,Siddhartha Bhattacharyya
Publisher : John Wiley & Sons
Page : 196 pages
File Size : 45,6 Mb
Release : 2020-06-02
Category : Computers
ISBN : 9781119551607

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Recent Advances in Hybrid Metaheuristics for Data Clustering by Sourav De,Sandip Dey,Siddhartha Bhattacharyya Pdf

An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors noted experts on the topic provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.

Metaheuristics for Data Clustering and Image Segmentation

Meera Ramadas,Ajith Abraham
Metaheuristics for Data Clustering and Image Segmentation Book PDF

Author : Meera Ramadas,Ajith Abraham
Publisher : Springer
Page : 163 pages
File Size : 55,9 Mb
Release : 2018-12-12
Category : Technology & Engineering
ISBN : 9783030040970

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Metaheuristics for Data Clustering and Image Segmentation by Meera Ramadas,Ajith Abraham Pdf

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.

Hybrid Metaheuristics

Thomas Bartz-Beielstein,Maria J. Blesa,Christian Blum,Boris Naujoks,Andrea Roli,Günther Rudolph,Michael Sampels
Hybrid Metaheuristics Book PDF

Author : Thomas Bartz-Beielstein,Maria J. Blesa,Christian Blum,Boris Naujoks,Andrea Roli,Günther Rudolph,Michael Sampels
Publisher : Springer
Page : 201 pages
File Size : 54,8 Mb
Release : 2007-09-20
Category : Computers
ISBN : 9783540755142

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Hybrid Metaheuristics by Thomas Bartz-Beielstein,Maria J. Blesa,Christian Blum,Boris Naujoks,Andrea Roli,Günther Rudolph,Michael Sampels Pdf

This book constitutes the refereed proceedings of the 4th International Workshop on Hybrid Metaheuristics, HM 2007, held in Dortmund, Germany. The 14 revised full papers discuss specific aspects of hybridization of metaheuristics, hybrid metaheuristics design, development and testing. With increasing attention to methodological aspects, from both the empirical and theoretical sides, the papers show a representative sample of research in the field of hybrid metaheuristics.

Cognitive Big Data Intelligence with a Metaheuristic Approach

Sushruta Mishra,Hrudaya Kumar Tripathy,Pradeep Kumar Mallick,Arun Kumar Sangaiah,Gyoo-Soo Chae
Cognitive Big Data Intelligence with a Metaheuristic Approach Book PDF

Author : Sushruta Mishra,Hrudaya Kumar Tripathy,Pradeep Kumar Mallick,Arun Kumar Sangaiah,Gyoo-Soo Chae
Publisher : Academic Press
Page : 374 pages
File Size : 42,7 Mb
Release : 2021-11-09
Category : Computers
ISBN : 9780323851183

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Cognitive Big Data Intelligence with a Metaheuristic Approach by Sushruta Mishra,Hrudaya Kumar Tripathy,Pradeep Kumar Mallick,Arun Kumar Sangaiah,Gyoo-Soo Chae Pdf

Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity. This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks. Provides a unique opportunity to present the work on the state-of-the-art of metaheuristics approach in the area of big data processing developing automated and intelligent models Explains different, feasible applications and case studies where cognitive computing can be successfully implemented in big data analytics using metaheuristics algorithms Provides a snapshot of the latest advances in the contribution of metaheuristics frameworks in cognitive big data applications to solve optimization problems

Metaheuristic Algorithms for Image Segmentation: Theory and Applications

Diego Oliva,Mohamed Abd Elaziz,Salvador Hinojosa
Metaheuristic Algorithms for Image Segmentation Theory and Applications Book PDF

Author : Diego Oliva,Mohamed Abd Elaziz,Salvador Hinojosa
Publisher : Springer
Page : 226 pages
File Size : 44,9 Mb
Release : 2019-03-02
Category : Technology & Engineering
ISBN : 9783030129316

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Metaheuristic Algorithms for Image Segmentation: Theory and Applications by Diego Oliva,Mohamed Abd Elaziz,Salvador Hinojosa Pdf

This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.

Comprehensive Metaheuristics

Seyedali Mirjalili,Amir Hossein Gandomi
Comprehensive Metaheuristics Book PDF

Author : Seyedali Mirjalili,Amir Hossein Gandomi
Publisher : Elsevier
Page : 468 pages
File Size : 44,7 Mb
Release : 2023-01-31
Category : Computers
ISBN : 9780323972673

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Comprehensive Metaheuristics by Seyedali Mirjalili,Amir Hossein Gandomi Pdf

Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applications across a variety of research fields. The book starts with fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation. After presenting multi-objective optimization, constrained optimization, and problem formation for metaheuristics, world-renowned authors give readers in-depth understanding of the full spectrum of algorithms and techniques. Scientists, researchers, academicians, and practitioners who are interested in optimizing a process or procedure to achieve a goal will benefit from the case studies of real-world applications from different domains. The book takes a much-needed holistic approach, putting the most widely used metaheuristic algorithms together with an in-depth treatise on multi-disciplinary applications of metaheuristics. Each algorithm is thoroughly analyzed to observe its behavior, providing a detailed tutorial on how to solve problems using metaheuristics. New case studies and research problem statements are also discussed, which will help researchers in their application of the concepts. Presented by world-renowned researchers and practitioners in metaheuristics Includes techniques, algorithms, and applications based on real-world case studies Presents the methodology for formulating optimization problems for metaheuristics Provides readers with methods for analyzing and tuning the performance of a metaheuristic, as well as for integrating metaheuristics in other AI techniques Features online complementary source code from the applications and algorithms

Partitional Clustering via Nonsmooth Optimization

Adil M. Bagirov,Napsu Karmitsa,Sona Taheri
Partitional Clustering via Nonsmooth Optimization Book PDF

Author : Adil M. Bagirov,Napsu Karmitsa,Sona Taheri
Publisher : Springer Nature
Page : 343 pages
File Size : 52,8 Mb
Release : 2020-02-24
Category : Technology & Engineering
ISBN : 9783030378264

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Partitional Clustering via Nonsmooth Optimization by Adil M. Bagirov,Napsu Karmitsa,Sona Taheri Pdf

This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.

Evolutionary Data Clustering: Algorithms and Applications

Ibrahim Aljarah,Hossam Faris,Seyedali Mirjalili
Evolutionary Data Clustering Algorithms and Applications Book PDF

Author : Ibrahim Aljarah,Hossam Faris,Seyedali Mirjalili
Publisher : Springer Nature
Page : 248 pages
File Size : 55,5 Mb
Release : 2021-02-20
Category : Technology & Engineering
ISBN : 9789813341913

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Evolutionary Data Clustering: Algorithms and Applications by Ibrahim Aljarah,Hossam Faris,Seyedali Mirjalili Pdf

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.

Metaheuristics for Machine Learning

Mansour Eddaly,Bassem Jarboui,Patrick Siarry
Metaheuristics for Machine Learning Book PDF

Author : Mansour Eddaly,Bassem Jarboui,Patrick Siarry
Publisher : Springer Nature
Page : 231 pages
File Size : 46,8 Mb
Release : 2023-03-13
Category : Computers
ISBN : 9789811938887

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Metaheuristics for Machine Learning by Mansour Eddaly,Bassem Jarboui,Patrick Siarry Pdf

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.

Handbook of Metaheuristic Algorithms

Chun-Wei Tsai,Ming-Chao Chiang
Handbook of Metaheuristic Algorithms Book PDF

Author : Chun-Wei Tsai,Ming-Chao Chiang
Publisher : Elsevier
Page : 624 pages
File Size : 41,7 Mb
Release : 2023-05-30
Category : Computers
ISBN : 9780443191091

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Handbook of Metaheuristic Algorithms by Chun-Wei Tsai,Ming-Chao Chiang Pdf

Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains. Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems. Presents a unified framework for metaheuristics and describes well-known algorithms and their variants Introduces fundamentals and advanced topics for solving engineering optimization problems, e.g., scheduling problems, sensors deployment problems, and clustering problems Includes source code based on the unified framework for metaheuristics used as examples to show how TS, SA, GA, ACO, PSO, DE, parallel metaheuristic algorithm, hybrid metaheuristic, local search, and other advanced technologies are realized in programming languages such as C++ and Python

Hybrid Quantum Metaheuristics

Siddhartha Bhattacharyya,Mario Köppen,Elizabeth Behrman,Ivan Cruz-Aceves
Hybrid Quantum Metaheuristics Book PDF

Author : Siddhartha Bhattacharyya,Mario Köppen,Elizabeth Behrman,Ivan Cruz-Aceves
Publisher : CRC Press
Page : 276 pages
File Size : 53,6 Mb
Release : 2022-05-07
Category : Technology & Engineering
ISBN : 9781000578157

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Hybrid Quantum Metaheuristics by Siddhartha Bhattacharyya,Mario Köppen,Elizabeth Behrman,Ivan Cruz-Aceves Pdf

The reference text introduces the principles of quantum mechanics to evolve hybrid metaheuristics-based optimization techniques useful for real world engineering and scientific problems. The text covers advances and trends in methodological approaches, theoretical studies, mathematical and applied techniques related to hybrid quantum metaheuristics and their applications to engineering problems. The book will be accompanied by additional resources including video demonstration for each chapter. It will be a useful text for graduate students and professional in the field of electrical engineering, electronics and communications engineering, and computer science engineering, this text: Discusses quantum mechanical principles in detail. Emphasizes the recent and upcoming hybrid quantum metaheuristics in a comprehensive manner. Provides comparative statistical test analysis with conventional hybrid metaheuristics. Highlights real-life case studies, applications, and video demonstrations.

Computational Intelligence for Wireless Sensor Networks

Sandip Kumar Chaurasiya,Joydeep Dutta,Arindam Biswas,Gorachand Dutta,Mrinal Kanti Sarkar
Computational Intelligence for Wireless Sensor Networks Book PDF

Author : Sandip Kumar Chaurasiya,Joydeep Dutta,Arindam Biswas,Gorachand Dutta,Mrinal Kanti Sarkar
Publisher : CRC Press
Page : 215 pages
File Size : 43,5 Mb
Release : 2022-07-25
Category : Computers
ISBN : 9781000594164

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Computational Intelligence for Wireless Sensor Networks by Sandip Kumar Chaurasiya,Joydeep Dutta,Arindam Biswas,Gorachand Dutta,Mrinal Kanti Sarkar Pdf

Computational Intelligence for Wireless Sensor Networks: Principles and Applications provides an integrative overview of the computational intelligence (CI) in wireless sensor networks and enabled technologies. It aims to demonstrate how the paradigm of computational intelligence can benefit Wireless Sensor Networks (WSNs) and sensor-enabled technologies to overcome their existing issues. This book provides extensive coverage of the multiple design challenges of WSNs and associated technologies such as clustering, routing, media access, security, mobility, and design of energy-efficient network operations. It also describes various CI strategies such as fuzzy computing, evolutionary computing, reinforcement learning, artificial intelligence, swarm intelligence, teaching learning-based optimization, etc. It also discusses applying the techniques mentioned above in wireless sensor networks and sensor-enabled technologies to improve their design. The book offers comprehensive coverage of related topics, including: Emergence of intelligence in wireless sensor networks Taxonomy of computational intelligence Detailed discussion of various metaheuristic techniques Development of intelligent MAC protocols Development of intelligent routing protocols Security management in WSNs This book mainly addresses the challenges pertaining to the development of intelligent network systems via computational intelligence. It provides insights into how intelligence has been pursued and can be further integrated in the development of sensor-enabled applications.

Unsupervised Classification

Sanghamitra Bandyopadhyay,Sriparna Saha
Unsupervised Classification Book PDF

Author : Sanghamitra Bandyopadhyay,Sriparna Saha
Publisher : Springer Science & Business Media
Page : 271 pages
File Size : 52,9 Mb
Release : 2012-12-13
Category : Computers
ISBN : 9783642324512

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Unsupervised Classification by Sanghamitra Bandyopadhyay,Sriparna Saha Pdf

Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.