Index
A
- advanced tools, for plotting PCA
- about / Advanced tools for plotting PCA
- agglomerative methods
- about / Hierarchical clustering
- amap
- ape
- arules
- arulesViz
- association rules
- fundamentals / Fundamentals of association rules
- representation / Representation
- plotting alternatives / Plotting alternatives for association rules
- by end-user tool / Association rules by end-user tool
- association rules model
- exploring / Exploring the association rules model
B
- barplots
- about / Barplots
- basic visualization
- data, exploring by / Exploring data by basic visualization
- biplot
- about / Principal component analysis
- boxplots
C
- car
- cba
- classes
- about / Supervised learning
- cluster
- clustering, by end-user interfaces
- clustering techniques
- fundamentals / Fundamentals of clustering techniques
- K-Means Clustering / The K-Means clustering
- hierarchical clustering / Hierarchical clustering
- cluster K-Mean algorithm
- defining / Defining the cluster K-Mean algorithm
- clusters
- plotting, alternatives / Alternatives for plotting clusters
- clustvarsel
- about / Wrapper methods
- URL / Chapter 6, Feature Selection Methods
- conventional metrics
- dependency metrics / Filter methods
- information metrics / Filter methods
- distance metrics / Filter methods
- correlation matrix
- used, for calculating principal components / Principal component analysis
- Correspondence Analysis (CA)
- about / Advanced tools for plotting PCA
- corrplot
- covariance matrix
- used, for calculating principal components / Principal component analysis
- CRAN
- about / Benefits of using R
- CRISP-DM
- curse of dimensionality
- about / The curse of dimensionality
D
- data
- exploring, by basic visualization / Exploring data by basic visualization
- relations, exploring in / Exploring relations in data, Exploring relations in data using Rattle
- exploring, by end-user interfaces / Exploration by end-user interfaces
- loading, into Rattle / Loading data into Rattle
- exploring, in Rattle / Basic exploration of dataset in Rattle
- exploring by graphs, in Rattle / Exploring data by graphs in Rattle
- transforming / Transforming data
- rescaling / Rescaling data
- data mining
- about / Data mining
- data mining methodology
- dataset
- loading / Loading a dataset
- exploring / Basic exploration of the dataset
- dendextend
- devtools
- distance metric
- about / Hierarchical clustering
- Euclidean Distance / Clustering distance metric
- Maximum Distance / Clustering distance metric
- Manhattan Distance / Clustering distance metric
- Canberra Distance / Clustering distance metric
- Binary Distance / Clustering distance metric
- Pearson Distance / Clustering distance metric
- Correlation / Clustering distance metric
- Spearman Distance / Clustering distance metric
- divisive methods
- about / Hierarchical clustering
- dplyr
E
- ElemStatLearn
- ellipse
- embedded methods, subset selection techniques
- about / Embedded methods
- end-user interfaces
- data, exploring by / Exploration by end-user interfaces
- entropy
- about / Entropy
- ewkm
- about / Embedded methods
- expert knowledge-based techniques
- exploratory data analysis
- about / Exploratory data analysis
F
- factoextra
- about / Advanced tools for plotting PCA
- reference link / Advanced tools for plotting PCA
- URL / Chapter 5, Dimensionality Reduction
- Factominer
- reference link / Principal components analysis by user interfaces
- FactoMineR
- Factoshiny
- fBasics
- feature extraction
- about / Feature extraction
- feature ranking
- about / Feature ranking
- feature selection techniques
- about / Feature selection techniques
- benefits / Feature selection techniques
- filter methods, subset selection techniques
- about / Filter methods
- fpc
- FSelector
G
- ggplot2
- gplots
H
- HCPC function
- 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
- HSAUR
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
- 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
- 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
- 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
- normalization techniques
- recenter / Recenter
- Scale [0-1] / Scale [0-1]
- Median/MAD / Median/MAD
- natural log / Natural log
P
- pastecs
- 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)
- 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
- 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
- Rcmmdr
- reference link / Principal components analysis by user interfaces
- Rcpp
- recenter
- about / Recenter
- relations
- exploring, in data / Exploring relations in data, Exploring relations in data using Rattle
- reshape
S
- Scale [0-1]
- about / Scale [0-1]
- scatterplot3d
- 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
- 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
W
- within-cluster sum of squares (WCSS)
- about / The K-Means clustering
- wrapper methods, subset selection techniques
- about / Wrapper methods
- wskm
X
- XLConnect