Data Clustering: Theory, Algorithms, and Applications, Chaoqun Ma, Guojun Gan, Jianhong Wu
Автор: Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii Tyshchenko Название: Self-Learning and Adaptive Algorithms for Business Applications: A Guide to Adaptive Neuro-Fuzzy Systems for Fuzzy Clustering Under Uncertainty Conditions ISBN: 1838671749 ISBN-13(EAN): 9781838671747 Издательство: Emerald Рейтинг: Цена: 62330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear.
Название: Partitional clustering algorithms ISBN: 3319092588 ISBN-13(EAN): 9783319092584 Издательство: Springer Рейтинг: Цена: 130610.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering.
Автор: Dmitri A. Viattchenin Название: A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications ISBN: 364244301X ISBN-13(EAN): 9783642443015 Издательство: Springer Рейтинг: Цена: 113180.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In a new approach to possibilistic clustering, the sought clustering structure of the set is based directly on the formal definition of fuzzy cluster and possibilistic memberships are determined directly from the values of the pairwise similarity of objects.
Автор: de Sourav, Dey Sandip, Bhattacharyya Siddhartha Название: Recent Advances in Hybrid Metaheuristics for Data Clustering ISBN: 1119551595 ISBN-13(EAN): 9781119551591 Издательство: Wiley Рейтинг: Цена: 110830.00 T Наличие на складе: Поставка под заказ. Описание:
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.
Автор: Christos H. Skiadas, James R. Bozeman Название: Data Analysis and Applications 1: Clustering and Regression, Modeling–estimating, Forecasting and Data Mining ISBN: 1786303825 ISBN-13(EAN): 9781786303820 Издательство: Wiley Рейтинг: Цена: 146730.00 T Наличие на складе: Невозможна поставка. Описание: Anglo-German Dramatic and Poetic Encounters contains essays focusing on the roles of drama and poetry in Anglo-German exchange in the Sattelzeit. It offers new perspectives on the movement of texts and ideas across genres and cultures, the formation and reception of poetic personae, and the place of illustration in cross-cultural, textual exchange.
Автор: Fausto Pedro Garcia Marquez Название: Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning ISBN: 179981565X ISBN-13(EAN): 9781799815655 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 166320.00 T Наличие на складе: Нет в наличии. Описание: As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.
Автор: Fausto Pedro Garcia Marquez Название: Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning ISBN: 1799801063 ISBN-13(EAN): 9781799801061 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 255030.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary.
Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.
Автор: ?gnes Vathy-Fogarassy; J?nos Abonyi Название: Graph-Based Clustering and Data Visualization Algorithms ISBN: 1447151577 ISBN-13(EAN): 9781447151579 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Vector Quantisation and Topology-Based Graph Representation
Graph-Based Clustering Algorithms
Graph-Based Visualisation of High-Dimensional Data
Автор: S. Dash, B.K. Tripathy Название: Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities ISBN: 1522563199 ISBN-13(EAN): 9781522563198 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 189420.00 T Наличие на складе: Невозможна поставка. Описание: As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the study and design of human systems helps provide efficient optimization algorithms.
Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities is an essential reference source that discusses applications of bio-inspired algorithms in data mining, computer vision, image processing, and pattern recognition, as well as methods of designing competent algorithms based on decomposition principles. Featuring research on topics such as cluster analysis, metaheuristic optimization, and image processing, this book is ideally designed for IT professionals, computer engineers, researchers, academicians, and upper-level students seeking coverage on how to develop efficient clustering algorithms.
Автор: Patrick Doreian Название: Advances in network clustering and blockmodeling / ISBN: 1119224705 ISBN-13(EAN): 9781119224709 Издательство: Wiley Рейтинг: Цена: 80200.00 T Наличие на складе: Поставка под заказ. Описание:
Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 years
This book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling.
Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more.
Offers a clear and insightful look at the state of the art in network clustering and blockmodeling
Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner
Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays
Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively
Written by leading contributors in the field of spatial networks analysis
Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.
Автор: Viattchenin Dmitri A Название: Heuristic Approach to Possibilistic Clustering: Algorithms a ISBN: 3642355358 ISBN-13(EAN): 9783642355356 Издательство: Springer Рейтинг: Цена: 130610.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In a new approach to possibilistic clustering, the sought clustering structure of the set is based directly on the formal definition of fuzzy cluster and possibilistic memberships are determined directly from the values of the pairwise similarity of objects.
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