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Spectral Clustering And Biclustering

Explores regular structures in graphs and contingency tables by spectral theory and statistical methods This book bridges the gap between;

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Test Data Engineering

This is the first technical book that considers tests as public tools and examines how to engineer and process test data, extract the;

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Clustering

mathematics of data recovery, and includes a new chapter covering more recent popular network clustering approaches-spectral, modularity and uniform;

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Clustering

mathematics of data recovery, and includes a new chapter covering more recent popular network clustering approaches-spectral, modularity and uniform;

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Applied Biclustering Methods for Big and High-Dimensional Data Using R

methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied;

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Data Clustering

clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains;

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Apache Mahout Clustering Designs

Explore clustering algorithms used with Apache Mahout About This Book * Use Mahout for clustering datasets and gain useful insights;

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Network Algorithms, Data Mining, and Applications

, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government;

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Network Algorithms, Data Mining, and Applications

, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government;

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Fundamentals of Data Analytics

and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral;

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Fundamentals of Data Analytics

and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral;

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Similarity-Based Pattern Recognition

; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.;

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Microarray Image and Data Analysis

Microarray Image and Data Analysis: Theory and Practice is a compilation of the latest and greatest microarray image and data analysis;

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Microarray Image and Data Analysis

Microarray Image and Data Analysis: Theory and Practice is a compilation of the latest and greatest microarray image and data analysis;

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Modern Algorithms of Cluster Analysis

. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences;

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Pattern Recognition

, spectral clustering, combining clustering algorithms Solutions manual, powerpoint slides, and additional resources are available to;

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Clustering

This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to;

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Modern Machine Learning Techniques and Their Applications in Cartoon Animation Research

the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble;

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Data Clustering in C++

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;

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Data Clustering in C++

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;

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Partitional Clustering Algorithms

This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The;

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Partitional Clustering via Nonsmooth Optimization

This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their;

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Partitional Clustering via Nonsmooth Optimization

This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their;

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Constrained Clustering

Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the;

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Fuzzy Sets & their Application to Clustering & Training

Fuzzy set theory - and its underlying fuzzy logic - represents one of the most significant scientific and cultural paradigms to emerge;

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Unsupervised Machine Learning for Clustering in Political and Social Research

skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program;

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Evolutionary Data Clustering

for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms;

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