Data Analysis in Bi-partial Perspective: Clustering and Beyond, Jan W. Owsi?ski

Автор: Meera Ramadas; Ajith Abraham
Название: Metaheuristics for Data Clustering and Image Segmentation
ISBN: 3030040968 ISBN-13(EAN): 9783030040963
Издательство: Springer
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Цена: 93160.00 T
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Описание: In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.

Автор: Lazzerini
Название: Fuzzy Sets & their Application to Clustering & Training
ISBN: 0849305896 ISBN-13(EAN): 9780849305894
Издательство: Taylor&Francis
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Цена: 178640.00 T
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Описание: Fuzzy logic applications allow uncertain or imprecise data to be clustered and analyzed when traditional methods cannot be used. This volume offers an introduction to fuzzy set theory and then progresses through the algorithms and techniques used to manipulate data using fuzzy sets, including classification, hierarchy, and cluster structure.

Автор: Felipe M. G. Fran?a; Alberto Ferreira de Souza
Название: Intelligent Text Categorization and Clustering
ISBN: 3642099297 ISBN-13(EAN): 9783642099298
Издательство: Springer
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Цена: 139750.00 T
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Описание: Spam filtering and web search are well-known applications of text categorization and clustering, but there are also many lesser known everyday uses. This text covers a wide spectrum of recent research developed for the field.

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: 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: 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: Alfredo Vellido; Karina Gibert; Cecilio Angulo; Jo
Название: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
ISBN: 3030196410 ISBN-13(EAN): 9783030196417
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), which was held in Barcelona, Spain, from the 26th to the 28th of June 2019. Since being founded in 1997, the conference has showcased the state of the art in unsupervised machine learning methods related to the successful and widely used self-organizing map (SOM) method, and extending its scope to clustering and data visualization. In this installment of the AISC series, the reader will find theoretical research on SOM, LVQ and related methods, as well as numerous applications to problems in fields ranging from business and engineering to the life sciences. Given the scope of its coverage, the book will be of interest to machine learning researchers and practitioners in general and, more specifically, to those looking for the latest developments in unsupervised learning and data visualization.

Автор: Viattchenin Dmitri A
Название: Heuristic Approach to Possibilistic Clustering: Algorithms a
ISBN: 3642355358 ISBN-13(EAN): 9783642355356
Издательство: Springer
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Цена: 130610.00 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: Felipe M. G. Fran?a; Alberto Ferreira de Souza
Название: Intelligent Text Categorization and Clustering
ISBN: 3540856439 ISBN-13(EAN): 9783540856436
Издательство: Springer
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Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Researchers have employed many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing. This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering.

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: 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
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.