Share

Practical Guide to Cluster Analysis in R

2017-08-23 Education
Download Practical Guide to Cluster Analysis in R PDF Online Free

Author :
Release : 2017-08-23
Genre : Education
Kind : eBook
Book Rating : 703/5 ( reviews)

GET EBOOK


Book Synopsis Practical Guide to Cluster Analysis in R by : Alboukadel Kassambara

Download or read book Practical Guide to Cluster Analysis in R written by Alboukadel Kassambara. This book was released on 2017-08-23. Available in PDF, EPUB and Kindle. Book excerpt: Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.

An Introduction to Clustering with R

2020-08-27 Mathematics
Download An Introduction to Clustering with R PDF Online Free

Author :
Release : 2020-08-27
Genre : Mathematics
Kind : eBook
Book Rating : 536/5 ( reviews)

GET EBOOK


Book Synopsis An Introduction to Clustering with R by : Paolo Giordani

Download or read book An Introduction to Clustering with R written by Paolo Giordani. This book was released on 2020-08-27. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.

Practical Guide To Principal Component Methods in R

2017-08-23 Education
Download Practical Guide To Principal Component Methods in R PDF Online Free

Author :
Release : 2017-08-23
Genre : Education
Kind : eBook
Book Rating : 136/5 ( reviews)

GET EBOOK


Book Synopsis Practical Guide To Principal Component Methods in R by : Alboukadel KASSAMBARA

Download or read book Practical Guide To Principal Component Methods in R written by Alboukadel KASSAMBARA. This book was released on 2017-08-23. Available in PDF, EPUB and Kindle. Book excerpt: Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.

Cluster Analysis

2011-01-14 Mathematics
Download Cluster Analysis PDF Online Free

Author :
Release : 2011-01-14
Genre : Mathematics
Kind : eBook
Book Rating : 449/5 ( reviews)

GET EBOOK


Book Synopsis Cluster Analysis by : Brian S. Everitt

Download or read book Cluster Analysis written by Brian S. Everitt. This book was released on 2011-01-14. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics. This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis. Key Features: Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies./li> Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.

Machine Learning Essentials

2018-03-10 Computers
Download Machine Learning Essentials PDF Online Free

Author :
Release : 2018-03-10
Genre : Computers
Kind : eBook
Book Rating : 857/5 ( reviews)

GET EBOOK


Book Synopsis Machine Learning Essentials by : Alboukadel Kassambara

Download or read book Machine Learning Essentials written by Alboukadel Kassambara. This book was released on 2018-03-10. Available in PDF, EPUB and Kindle. Book excerpt: Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: - Covers machine learning algorithm and implementation - Key mathematical concepts are presented - Short, self-contained chapters with practical examples.

You may also like...