Fuzzy Collaborative Forecasting and Clustering, Tin-Chih Toly Chen; Katsuhiro Honda
Автор: Zeshui Xu Название: Intuitionistic Fuzzy Aggregation and Clustering ISBN: 3642436129 ISBN-13(EAN): 9783642436123 Издательство: Springer Рейтинг: Цена: 21661.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An inclusive primer on intuitionistic fuzzy clustering algorithms, this volume covers priority theory and methods of intuitionistic preference relations. It also shows how fuzzy algorithms can be applied to practicalities such as supply-chain management.
Автор: Saman K. Halgamuge; Lipo Wang Название: Classification and Clustering for Knowledge Discovery ISBN: 3642065422 ISBN-13(EAN): 9783642065422 Издательство: Springer Рейтинг: Цена: 29209.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees.
Автор: Lazzerini Название: Fuzzy Sets & their Application to Clustering & Training ISBN: 0849305896 ISBN-13(EAN): 9780849305894 Издательство: Taylor&Francis Рейтинг: Цена: 26796.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: 3540856439 ISBN-13(EAN): 9783540856436 Издательство: Springer Рейтинг: Цена: 20962.00 р. 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: 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: 13974.00 р. 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? .
Описание: This book puts forward a new method for solving the text document (TD) clustering problem, which is established in two main stages: (i) A new feature selection method based on a particle swarm optimization algorithm with a novel weighting scheme is proposed, as well as a detailed dimension reduction technique, in order to obtain a new subset of more informative features with low-dimensional space. This new subset is subsequently used to improve the performance of the text clustering (TC) algorithm and reduce its computation time. The k-mean clustering algorithm is used to evaluate the effectiveness of the obtained subsets. (ii) Four krill herd algorithms (KHAs), namely, the (a) basic KHA, (b) modified KHA, (c) hybrid KHA, and (d) multi-objective hybrid KHA, are proposed to solve the TC problem; each algorithm represents an incremental improvement on its predecessor. For the evaluation process, seven benchmark text datasets are used with different characterizations and complexities.Text document (TD) clustering is a new trend in text mining in which the TDs are separated into several coherent clusters, where all documents in the same cluster are similar. The findings presented here confirm that the proposed methods and algorithms delivered the best results in comparison with other, similar methods to be found in the literature.
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: 20962.00 р. 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.
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
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: Viattchenin Dmitri A Название: Heuristic Approach to Possibilistic Clustering: Algorithms a ISBN: 3642355358 ISBN-13(EAN): 9783642355356 Издательство: Springer Рейтинг: Цена: 19591.00 р. 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: Olfa Nasraoui; Chiheb-Eddine Ben N`Cir Название: Clustering Methods for Big Data Analytics ISBN: 3319978632 ISBN-13(EAN): 9783319978635 Издательство: Springer Рейтинг: Цена: 20962.00 р. 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: Junjie Wu Название: Advances in K-means Clustering ISBN: 3642447570 ISBN-13(EAN): 9783642447570 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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