Data Analysis and Applications 1: Clustering and Regression, Modeling–estimating, Forecasting and Data Mining, Christos H. Skiadas, James R. Bozeman

Автор: Mirkin, Boris
Название: Clustering for Data Mining
ISBN: 1584885343 ISBN-13(EAN): 9781584885344
Издательство: Taylor&Francis
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Цена: 61240.00 T
Наличие на складе: Нет в наличии.
Описание: Presents a theory that not only closes gaps in K-Means and Ward methods, but also extends them into areas of interest, such as clustering mixed scale data and incomplete clustering. This work suggests methods for both cluster finding and cluster description, and includes nearly 60 computational examples covering the various stages of clustering.

Автор: Guojun Gan
Название: Data Clustering: Theory, Algorithms, and Applications
ISBN: 0898716233 ISBN-13(EAN): 9780898716238
Издательство: Mare Nostrum (Eurospan)
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Цена: 112860.00 T
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Описание: Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, centre-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Suitable as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining.

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
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Цена: 62330.00 T
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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.

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)
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Цена: 166320.00 T
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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)
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Цена: 255030.00 T
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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: Isra?l C?sar Lerman
Название: Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering
ISBN: 1447167910 ISBN-13(EAN): 9781447167914
Издательство: Springer
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Цена: 153720.00 T
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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

Автор: Jan W. Owsi?ski
Название: Data Analysis in Bi-partial Perspective: Clustering and Beyond
ISBN: 3030133885 ISBN-13(EAN): 9783030133887
Издательство: Springer
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Цена: 93160.00 T
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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. It is based on adequate representation of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis.The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard “academic” manner.

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)
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Цена: 189420.00 T
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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: Jin Cheqing, Zhou Aoying, Mao Jiali
Название: Clustering And Outlier Detection For Trajectory Stream Data
ISBN: 9811210454 ISBN-13(EAN): 9789811210457
Издательство: World Scientific Publishing
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Цена: 95040.00 T
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Описание:

As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.

Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.

This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.

Автор: V.S. Kumbhar, K.S. Oza, R.K. Kamat
Название: Web Mining: A Synergic Approach Resorting to Classification and Clustering
ISBN: 8793379838 ISBN-13(EAN): 9788793379831
Издательство: Taylor&Francis
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Цена: 73490.00 T
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Описание: Showcases an effective methodology for classification and clustering of web sites from a usability point of view. While the clustering and classification is accomplished by using an open source tool, WEKA, the basic dataset for the selected websites has been arrived at by using a free tool site-analyser. As a case study, several commercial websites are analysed.

Автор: Dmitri A. Viattchenin
Название: A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
ISBN: 364244301X ISBN-13(EAN): 9783642443015
Издательство: Springer
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Цена: 113180.00 T
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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: Tin-Chih Toly Chen; Katsuhiro Honda
Название: Fuzzy Collaborative Forecasting and Clustering
ISBN: 3030225739 ISBN-13(EAN): 9783030225735
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book introduces the basic concepts of fuzzy collaborative forecasting and clustering, including its methodology, system architecture, and applications. It demonstrates how dealing with disparate data sources is becoming more and more popular due to the increasing spread of internet applications. The book proposes the concepts of collaborative computing intelligence and collaborative fuzzy modeling, and establishes several so-called fuzzy collaborative systems. It shows how technical constraints, security issues, and privacy considerations often limit access to some sources. This book is a valuable source of information for postgraduates, researchers and fuzzy control system developers, as it presents a very effective fuzzy approach that can deal with disparate data sources, big data, and multiple expert decision making.