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An Introduction to Clustering with R

accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life;

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Unsupervised Machine Learning for Clustering in Political and Social Research

. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for;

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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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Biostatistics with R: An Introduction to Statistics Through Biological Data

book is an introduction to Bayesian analysis. This author discusses basic statistical analysis through a series of biological examples using;

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Partitional Clustering via Nonsmooth Optimization

challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to;

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Partitional Clustering via Nonsmooth Optimization

challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to;

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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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Data Clustering

, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB(R) programming languages.;

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Applied Unsupervised Learning with R

, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how;

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Text Mining with MATLAB (R)

. The first part provides an introduction to basic procedures for handling and operating with text strings. Then, it reviews major mathematical;

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Chemometrics with R

This book offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a comprehensive;

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Integrative Cluster Analysis in Bioinformatics

emphasis on gene expression analysis * Provides an excellent introduction to molecular biology with computer scientists and information engineering;

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Applied Multivariate Statistics with R

experience with R is not necessary. New to this edition are chapters devoted to longitudinal studies and the clustering of large data. It is an;

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Fuzzy Sets & their Application to Clustering & Training

Clustering and Training offers a comprehensive introduction to fuzzy set theory, focusing on the concepts and results needed for training and;

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LargeScale Structure Of The Universe

theories of how the clustering evolves in an expanding universe. In Chapter One the author provides an historical introduction to the subject;

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Constrained Clustering

book ends by applying clustering with constraints to relational data, privacy-preserving data publishing, and video surveillance data. It;

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Elements of Micropalaeontology

, development and maintenance life cycles. Starting with the fundamentals, an introduction to the concepts of the various hardware architectures and the;

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An Introduction to R

This manual provides an introduction to R , a software package for statistical computing and graphics. R is free software, distributed;

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Model-Based Clustering and Classification for Data Science

the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern;

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Pro Linux High Availability Clustering

, you'll be able to efficiently apply Linux HA to your work situation with confidence. Author Sander Van Vugt teaches Linux high-availability;

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Introduction To Percolation Theory

This work dealing with percolation theory clustering, criticallity, diffusion, fractals and phase transitions takes a broad approach to the;

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Data Mining & Predictive Analytics

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data;

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Correspondence Analysis and Data Coding with Java and R

clustering. The focus then shifts to data coding, with a survey of the widely varied possibilities correspondence analysis offers and introduction;

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Correspondence Analysis and Data Coding with Java and R

clustering. The focus then shifts to data coding, with a survey of the widely varied possibilities correspondence analysis offers and introduction;

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Introduction to Clustering Large and High-Dimensional Data

the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure;

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An Introduction to Bootstrap Methods with Applications to R

of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented;

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Handbook of Cluster Analysis

book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are;

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