Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, Alfredo Vellido; Karina Gibert; Cecilio Angulo; Jo
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.
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: Francesco Masulli; Alfredo Petrosino; Stefano Rove Название: Clustering High--Dimensional Data ISBN: 3662485761 ISBN-13(EAN): 9783662485767 Издательство: Springer Рейтинг: Цена: 5590.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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 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: Thomas Villmann; Frank-Michael Schleif; Marika Kad Название: Advances in Self-Organizing Maps and Learning Vector Quantization ISBN: 3319076949 ISBN-13(EAN): 9783319076942 Издательство: Springer Рейтинг: Цена: 26122.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need.- Dynamic formation of self-organizing maps.- MS-SOM: Magnitude Sensitive Self-Organizing Maps.- Bagged Kernel SOM.- Probability ridges and distortion flows: Visualizing multivariate time series using a variational Bayesian manifold learning method.- Short review of dimensionality reduction methods based on stochastic neighbour embedding.- Attention based Classification Learning in GLVQ and Asymmetric Classification Error Assessment.-Visualization and Classification of DNA sequences using Pareto learning Self Organizing Maps based on Frequency and Correlation Coefficient.- Probabilistic prototype classification using t-norms.- Rejection Strategies for Learning Vector Quantization - a Comparison of Probabilistic and Deterministic Approaches.- Comparison of spectrum cluster analysis with PCA and spherical SOM and related issues not amenable to PCA.- Exploiting the structures of the U-matrix.- Partial Mutual Information for Classification Analysis of Gene expression Data by Learning Vector Quantization.- Composition of Learning Patterns using Spherical Self-Organizing Maps in Image Analysis with Subspace Classifier.- Self-Organizing Map for the Prize-Collecting Traveling Salesman Problem.- A Survey of SOM-based Active Contour Models for Image Segmentation.- Biologically Plausible SOM Representation of the Orthographic Form of 50,000 French Words.- Prototype-based classifiers and their application in the life sciences.- Generative versus discriminative prototype based classification.- Some room for GLVQ: Semantic Labeling of occupancy grid maps.- Anomaly detection based on confidence intervals using SOM with an application to Health Monitoring.- RFSOM - Extending Self-Organizing feature Maps with adaptive metrics to combine spatial and textural features for body pose estimation.- Beyond Standard Metrics - On the Selection and Combination of Distance Metrics for an Improved.- Classification of Hyperspectral Data.- The Sky Is Not the Limit.- Development of Target Reaching Gesture Map in the Cortex and Its Relation to the Motor Map: A Simulation Study.- A Concurrent SOM-based Chan-Vese Model for Image Segmentation.- Text mining of life-philosophicl insights.- SOMbrero: an R Package for Numeric and Non-numeric Self-Organizing Maps.- K-Nearest Neighbor Nonnegative Matrix Factorization for Learning a Mixture of Local SOM Models.
Автор: Meera Ramadas; Ajith Abraham Название: Metaheuristics for Data Clustering and Image Segmentation ISBN: 3030040968 ISBN-13(EAN): 9783030040963 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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