unsupervised ML in r

Unsupervised Learning with R Table of Contents Unsupervised Learning with R Credits About the Author Acknowledgments About the Reviewer www.PacktPub.com Support files, eBooks, discount offers, and more Why subscribe? Free access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions Welcome to the Age of Information Technology The information age Data mining Machine learning Supervised learning Unsupervised learning Information theory Entropy Information gain Data mining methodology and software tools CRISP-DM Benefits of using R Summary Working with Data – Exploratory Data Analysis Exploratory data analysis Loading a dataset Basic exploration of the dataset Exploring data by basic visualization Histograms Barplots Boxplots Special visualizations Exploring relations in data Exploration by end-user interfaces Loading data into Rattle Basic exploration of dataset in Rattle Exploring data by graphs in Rattle Exploring relations in data using Rattle Summary Identifying and Understanding Groups – Clustering Algorithms Transforming data Rescaling data Recenter Scale [0-1] Median/MAD Natural log Imputation of missing data Zero/Missing Mean imputation Fundamentals of clustering techniques The K-Means clustering Defining the number of clusters Defining the cluster K-Mean algorithm Alternatives for plotting clusters Hierarchical clustering Clustering distance metric Linkage methods Hierarchical clustering in R Hierarchical clustering with factors Tips for choosing a hierarchical clustering algorithm Plotting alternatives for hierarchical clustering Clustering by end-user interfaces Summary Association Rules Fundamentals of association rules Representation Exploring the association rules model Plotting alternatives for association rules Association rules by end-user tool Summary Dimensionality Reduction The curse of dimensionality Feature extraction Principal component analysis Additional visual support for PCA Advanced tools for plotting PCA Hierarchical clustering on principal components Principal components analysis by user interfaces Summary Feature Selection Methods Feature selection techniques Expert knowledge-based techniques Feature ranking Subset selection techniques Embedded methods Wrapper methods Filter methods Summary A References Chapter 1, Welcome to the Age of Information Technology Chapter 2, Working with Data – Exploratory Data Analysis Chapter 3, Identifying and Understanding Groups – Clustering Algorithms Chapter 4, Association Rules Chapter 5, Dimensionality Reduction Chapter 6, Feature Selection Methods Index Unsupervised Learning with R G ggplot2 URL / Chapter 2, Working with Data – Exploratory Data Analysis gplots URL / Chapter 2, Working with Data – Exploratory Data Analysis H HCPC function about / Hierarchical clustering on principal components hierarchical clustering about / Hierarchical clustering distance metric, clustering / Clustering distance metric linkage methods / Linkage methods in R / Hierarchical clustering in R with factors / Hierarchical clustering with factors tips, for selecting / Tips for choosing a hierarchical clustering algorithm plotting alternatives / Plotting alternatives for hierarchical clustering on principal components / Hierarchical clustering on principal components Hierarchical Clustering Analysis (HCA) about / Hierarchical clustering histogram about / Histograms building / Histograms Hmisc URL / Chapter 2, Working with Data – Exploratory Data Analysis HSAUR URL / Chapter 3, Identifying and Understanding Groups – Clustering Algorithms I imputation missing data about / Imputation of missing data Zero/Missing / Zero/Missing mean imputation / Mean imputation information age about / The information age information gain about / Information gain information theory about / Information theory Iris Dataset URL / Chapter 2, Working with Data – Exploratory Data Analysis Iris dataset about / Loading a dataset K K-Means Clustering about / The K-Means clustering clusters number, defining / Defining the number of clusters L labels about / Supervised learning lattice URL / Chapter 2, Working with Data – Exploratory Data Analysis linkage method about / Hierarchical clustering linkage methods about / Linkage methods Single Linkage / Linkage methods Complete Linkage / Linkage methods Average Linkage / Linkage methods Centroid Linkage / Linkage methods Median Linkage / Linkage methods Ward Linkage / Linkage methods McQuitty Linkage / Linkage methods M machine learning about / Machine learning supervised learning / Supervised learning unsupervised learning / Unsupervised learning mclust URL / Chapter 6, Feature Selection Methods mean imputation about / Mean imputation Median/MAD about / Median/MAD Multiple Correspondence Analysis (MCA) about / Advanced tools for plotting PCA N natural log about / Natural log NbClust URL / Chapter 3, Identifying and Understanding Groups – Clustering Algorithms normalization techniques recenter / Recenter Scale [0-1] / Scale [0-1] Median/MAD / Median/MAD natural log / Natural log P pastecs URL / Chapter 2, Working with Data – Exploratory Data Analysis plotting alternatives, for association rules / Plotting alternatives for association rules Principal component analysis (PCA) about / Principal component analysis visual support / Additional visual support for PCA advanced tools, for plotting / Advanced tools for plotting PCA by user interfaces / Principal components analysis by user interfaces Principal Component Analysis (PCA) about / Principal component analysis, Advanced tools for plotting PCA principal components calculating / Principal component analysis calculating, correlation matrix used / Principal component analysis calculating, covariance matrix used / Principal component analysis princomp about / Principal component analysis R R benefits / Benefits of using R URL / Chapter 1, Welcome to the Age of Information Technology Rattle data, loading into / Loading data into Rattle data, exploring in / Basic exploration of dataset in Rattle data, exploring by graphs / Exploring data by graphs in Rattle relations, exploring in data / Exploring relations in data using Rattle URL / Chapter 2, Working with Data – Exploratory Data Analysis, Chapter 3, Identifying and Understanding Groups – Clustering Algorithms Rcmdr URL / Chapter 5, Dimensionality Reduction Rcmmdr reference link / Principal components analysis by user interfaces Rcpp URL / Chapter 5, Dimensionality Reduction recenter about / Recenter relations exploring, in data / Exploring relations in data, Exploring relations in data using Rattle reshape URL / Chapter 3, Identifying and Understanding Groups – Clustering Algorithms S Scale [0-1] about / Scale [0-1] scatterplot3d URL / Chapter 3, Identifying and Understanding Groups – Clustering Algorithms silhouette graphics reference link / Alternatives for plotting clusters singular value decomposition (SVD) about / Principal component analysis software tools, data mining CRISP-DM / CRISP-DM special visualizations about / Special visualizations SphericalCubature about / The curse of dimensionality stringi URL / Chapter 5, Dimensionality Reduction subset selection techniques about / Subset selection techniques embedded methods / Embedded methods wrapper methods / Wrapper methods filter methods / Filter methods supervised learning about / Supervised learning models / Supervised learning modeling stage / Supervised learning predicting stage / Supervised learning T teachers about / Supervised learning U UCI Machine Learning Repository reference link / Hierarchical clustering in R unsupervised learning about / Unsupervised learning V visual support, on PCA about / Additional visual support for PCA W within-cluster sum of squares (WCSS) about / The K-Means clustering wrapper methods, subset selection techniques about / Wrapper methods wskm URL / Chapter 6, Feature Selection Methods X XLConnect URL / Chapter 2, Working with Data – Exploratory Data Analysis ... Hierarchical clustering in R Hierarchical clustering with factors Tips for choosing a hierarchical clustering algorithm Plotting alternatives for hierarchical clustering Clustering by end-user interfaces Summary... An adequate knowledge of data, by exploration, is essential in order to apply unsupervised learning algorithms correctly This assertion is true for any effort in data mining, not just for unsupervised learning Chapter 3, Identifying and Understanding Groups – Clustering Algorithms, teaches the... The K-Means clustering Defining the number of clusters Defining the cluster K-Mean algorithm Alternatives for plotting clusters Hierarchical clustering Clustering distance metric Linkage methods Hierarchical clustering in R