Relational Data Clustering, Long, Bo , Zhang, Zhongfei , Yu, Philip S.
Автор: Olfa Nasraoui; Chiheb-Eddine Ben N`Cir Название: Clustering Methods for Big Data Analytics ISBN: 3030074196 ISBN-13(EAN): 9783030074197 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
Автор: Olfa Nasraoui; Chiheb-Eddine Ben N`Cir Название: Clustering Methods for Big Data Analytics ISBN: 3319978632 ISBN-13(EAN): 9783319978635 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
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.
Автор: Lei Meng; Ah-Hwee Tan; Donald C. Wunsch II Название: Adaptive Resonance Theory in Social Media Data Clustering ISBN: 3030029840 ISBN-13(EAN): 9783030029845 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:Basic knowledge (data & challenges) on social media analyticsClustering as a fundamental technique for unsupervised knowledge discovery and data miningA class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domainAdaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction.It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks.Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:How to process big streams of multimedia data?How to analyze social networks with heterogeneous data?How to understand a user’s interests by learning from online posts and behaviors?How to create a personalized search engine by automatically indexing and searching multimodal information resources? .
Автор: Gan, Guojun Название: Data Clustering in C++ ISBN: 0367382954 ISBN-13(EAN): 9780367382957 Издательство: Taylor&Francis Рейтинг: Цена: 65320.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms.
Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered.
This book is divided into three parts--
Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns
A C++ Data Clustering Framework: The development of data clustering base classes
Data Clustering Algorithms: The implementation of several popular data clustering algorithms
A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the downloadable resources. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.
Автор: Seetha Hari, Murty M. Narasimha, Tripathy B. K. Название: Modern Technologies for Big Data Classification and Clustering ISBN: 1522528059 ISBN-13(EAN): 9781522528050 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 208830.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage.Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics.Topics Covered:The many academic areas covered in this publication include, but are not limited to:Data visualizationDistributed Computing SystemsOpinion MiningPrivacy and securityRisk analysisSocial Network AnalysisText Data AnalyticsWeb Data Analytics
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 Рейтинг: Цена: 153720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: 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 Рейтинг: Цена: 73490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Ujjwal Maulik; Sanghamitra Bandyopadhyay; Anirban Название: Multiobjective Genetic Algorithms for Clustering ISBN: 3642439632 ISBN-13(EAN): 9783642439636 Издательство: Springer Рейтинг: Цена: 51200.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book covers clustering using multiobjective genetic algorithms, with extensive real-life application in data mining and bioinformatics. The authors offer instructions for relevant techniques, and demonstrate real-world applications in several disciplines.
Автор: Tin-Chih Toly Chen; Katsuhiro Honda Название: Fuzzy Collaborative Forecasting and Clustering ISBN: 3030225739 ISBN-13(EAN): 9783030225735 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Mirkin, Boris Название: Clustering ISBN: 036738079X ISBN-13(EAN): 9780367380793 Издательство: Taylor&Francis Рейтинг: Цена: 65320.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Often considered more of an art than a science, books on clustering have been dominated by learning through example with techniques chosen almost through trial and error. Even the two most popular, and most related, clustering methods-K-Means for partitioning and Ward's method for hierarchical clustering-have lacked the theoretical underpinning required to establish a firm relationship between the two methods and relevant interpretation aids. Other approaches, such as spectral clustering or consensus clustering, are considered absolutely unrelated to each other or to the two above mentioned methods.
Clustering: A Data Recovery Approach, Second Edition presents a unified modeling approach for the most popular clustering methods: the K-Means and hierarchical techniques, especially for divisive clustering. It significantly expands coverage of the mathematics of data recovery, and includes a new chapter covering more recent popular network clustering approaches-spectral, modularity and uniform, additive, and consensus-treated within the same data recovery approach. Another added chapter covers cluster validation and interpretation, including recent developments for ontology-driven interpretation of clusters. Altogether, the insertions added a hundred pages to the book, even in spite of the fact that fragments unrelated to the main topics were removed.
Illustrated using a set of small real-world datasets and more than a hundred examples, the book is oriented towards students, practitioners, and theoreticians of cluster analysis. Covering topics that are beyond the scope of most texts, the author's explanations of data recovery methods, theory-based advice, pre- and post-processing issues and his clear, practical instructions for real-world data mining make this book ideally suited for teaching, self-study, and professional reference.
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.
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