Описание: This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications.
Автор: M. Emre Celebi Название: Partitional Clustering Algorithms ISBN: 3319347985 ISBN-13(EAN): 9783319347981 Издательство: Springer Рейтинг: Цена: 16977.00 р. 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.
Название: Partitional clustering algorithms ISBN: 3319092588 ISBN-13(EAN): 9783319092584 Издательство: Springer Рейтинг: Цена: 19591.00 р. 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.
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: 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: 16624.00 р. 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: Patrick Doreian Название: Advances in network clustering and blockmodeling / ISBN: 1119224705 ISBN-13(EAN): 9781119224709 Издательство: Wiley Рейтинг: Цена: 12030.00 р. 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: Zacharoula Andreopoulou; Christiana Koliouska; Con Название: Multicriteria and Clustering ISBN: 3319555642 ISBN-13(EAN): 9783319555645 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Further, it presents some of the most common methodologies for statistical analysis and mathematical modeling, and discusses in detail ten examples that explain and show "hands-on" how operational research can be used in key decision-making processes at enterprises in the agricultural food and environmental industries.
Автор: Ronis D Название: Clustering Standards in Integrated Units: Second Edition ISBN: 1412955564 ISBN-13(EAN): 9781412955560 Издательство: Sage Publications Рейтинг: Цена: 10296.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides teachers with a framework for designing, implementing, and evaluating interdisciplinary units that integrate content and standards across multiple curriculum areas.
Описание: This book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms.
Описание: Preface.- On Some Facets of the Partition Set of a Finite Set.- Two Methods of Non-hierarchical Clustering.- Structure and Mathematical Representation of Data.- Ordinal and Metrical Analysis of the Resemblance Notion.- Comparing Attributes by a Probabilistic and Statistical Association I.- Comparing Attributes by a Probabilistic and Statistical Association II.- Comparing Objects or Categories Described by Attributes.- The Notion of "Natural" Class, Tools for its Interpretation. The Classifiability Concept.- Quality Measures in Clustering.- Building a Classification Tree.- Applying the LLA Method to Real Data.- Conclusion and Thoughts for Future Works
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