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Clustering

data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors;

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Introduction to Clustering Large and High-Dimensional Data

or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might;

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Foundations of Data Science

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high;

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Data Clustering

, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as;

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High Dimensional Econometrics and Identification

exception. One of the tools to analyze large, high-dimensional data is the panel data model.High-Dimensional Econometrics and Identification grew;

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A Graph Theoretic Approach to Heterogeneous Data Clustering

is threefold: homogeneous clustering of images, pairwise heterogeneous data co-clustering, and high-order star-structured heterogeneous data co;

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Model-Based Clustering and Classification for Data Science

approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering;

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Manifold Learning Theory and Applications

Trained to extract actionable information from large volumes of high-dimensional data, engineers and scientists often have trouble;

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Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R

Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline;

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Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R

Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline;

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Projection Based Clustering through Self Organization and Swarm Intelligence

landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are;

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Statistical Foundations of Data Science

Provides theoretical insights and justification of the statistical procedures for the analysis of high-dimensional data Presents a general;

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Apache Mahout Clustering Designs

in the field of machine learning to better analyse your data and get useful insights into it. Starting with the introduction of clustering;

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Multivariate Statistics

Written by well-known, award-winning authors, this is the first book to focus on high-dimensional data analysis while presenting real-world;

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Data Science

. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and;

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Advanced Analysis of Gene Expression Microarray Data

data visualization. The visualization process is intended to transform the gene expression data set from high-dimensional space into a more;

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Gaussian Process Regression Analysis for Functional Data

methodological developments for high dimensional data and variable selection. The remainder of the text explores advanced topics of functional regression;

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Exploration and Analysis of DNA Microarray and Other High-Dimensional Data

of Environmental Quality Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage;

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High-Dimensional Covariance Estimation

. High-Dimensional Covariance Estimation features chapters on: * Data, Sparsity, and Regularization * Regularizing the Eigenstructure * Banding;

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Statistical Bioinformatics

detailedpresentation, discussion of limitations, and problems. The bookstarts with an introduction to probability and statistics forgenome-wide data, and moves;

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Nature Inspired Computing for Data Science

health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep;

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Data Mining and Data Visualization

is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes;

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High-Dimensional Statistics

introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core;

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The Unsupervised Learning Workshop

and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you'll use t-SNE models to extract high;

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High Frequency Financial Econometrics

, bond and foreign exchange markets, and large dimensional volatility modeling. The volume is of interest to graduate students, researchers, and;

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Advances in Knowledge Discovery and Data Mining

uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional, and;

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Advances in Knowledge Discovery and Data Mining

uncertain and imprecise data; mining temporal and spatial data; feature extraction and selection; mining heterogeneous, high-dimensional and;

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