Clustering And Outlier Detection For Trajectory Stream Data, Jin Cheqing, Zhou Aoying, Mao Jiali

Автор: Olfa Nasraoui; Chiheb-Eddine Ben N`Cir
Название: Clustering Methods for Big Data Analytics
ISBN: 3030074196 ISBN-13(EAN): 9783030074197
Издательство: Springer
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Цена: 139750.00 T
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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: 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
Наличие на складе: Невозможна поставка.
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: 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: Thomas Villmann; M. Biehl; Barbara Hammer; Michel
Название: Similarity-Based Clustering
ISBN: 3642018041 ISBN-13(EAN): 9783642018046
Издательство: Springer
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Цена: 95770.00 T
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Описание: Recent Developments and Biomedical Applications. .

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
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Цена: 93160.00 T
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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: 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)
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Цена: 208830.00 T
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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: Olfa Nasraoui; Chiheb-Eddine Ben N`Cir
Название: Clustering Methods for Big Data Analytics
ISBN: 3319978632 ISBN-13(EAN): 9783319978635
Издательство: Springer
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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: Francesco Masulli; Alfredo Petrosino; Stefano Rove
Название: Clustering High--Dimensional Data
ISBN: 3662485761 ISBN-13(EAN): 9783662485767
Издательство: Springer
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Цена: 37270.00 T
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Описание: This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering.

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: 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.

Автор: Ujjwal Maulik; Sanghamitra Bandyopadhyay; Anirban
Название: Multiobjective Genetic Algorithms for Clustering
ISBN: 3642439632 ISBN-13(EAN): 9783642439636
Издательство: Springer
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Цена: 51200.00 T
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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: Junjie Wu
Название: Advances in K-means Clustering
ISBN: 3642447570 ISBN-13(EAN): 9783642447570
Издательство: Springer
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Цена: 102480.00 T
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Описание: The K-means algorithm is commonly used in data mining and business intelligence. This award-winning research pioneers its application to the intricacies of `big data`, detailing a theoretical framework for aggregating and validating clusters with K-means.