[PDF] [PDF] Practical Guide to Principal Component Methods in R - Datanovia

IV Clustering 141 8 HCPC: Hierarchical Clustering on Principal Components Previously, we published a book entitled “Practical Guide To Cluster Analysis in



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[PDF] Practical Guide To Cluster Analysis in R - Datanovia

K-means clustering (Chapter 4), • K-Medoids or PAM (partitioning around medoids) algorithm (Chapter 5) and • CLARA algorithms (Chapter 6) Partitioning 



[PDF] Practical Guide to Principal Component Methods in R - Datanovia

IV Clustering 141 8 HCPC: Hierarchical Clustering on Principal Components Previously, we published a book entitled “Practical Guide To Cluster Analysis in



[PDF] Practical Guide To Cluster Analysis In R Unsupervised Machine

Datanovia Machine Learning Articles Sthda Practical Guide To Cluster Analysis In R Book R Bloggers Customer Reviews Practical Guide To Cluster



[PDF] clValid, an R package for cluster validation

The user can choose from nine clustering algorithms in existing R pack- ages, including hierarchical, K-means, self-organizing maps (SOM), 1 Page 2 and model 

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41.2%
18.4% 12.4%

8.2%7%4.2%3%2.7%1.6%

1.2%

01020304050

1 2 3 45 6 7 89 10

Dimensions

Percentage of explained variances

Scree plot

X100m

Long.jump

Shot.put

High.jump

X400m

X110m.hurdle

Discus

Pole.vault

Javeline

X1500m

-1.0-0.50.00.51.0 -1.0 -0.50.0 0.51.0

Dim1 (41.2%)

Dim2 (18.4%)

681012

contrib

Variables - PCA

051015

X100m

Long.jump

X110m.hurdle

Discus

Shot.put

X400m

High.jump

Javeline

Pole.vault

X1500m

Contributions (%)

Contribution to Dim 1

0102030

Pole.vault

X1500m

High.jump

Javeline

X400m

Long.jump

X100m

Shot.put

Discus

X110m.hurdle

Contributions (%)

Contribution to Dim 2

SEBRLE

CLAY

BERNARD

YURKOV

ZSIVOCZKY

McMULLEN

MARTINEAU

HERNU

BARRAS

NOOL

BOURGUIGNON

Sebrle

Clay

Karpov

Macey

Warners

Zsivoczky

Hernu

Bernard

Schwarzl

Pogorelov

Schoenbeck

Barras

-2-1012 -2.5 0.02.5

Dim1 (41.2%)

Dim2 (18.4%)

0.250.500.75

cos2

Individuals - PCA

SEBRLECLAY

BERNARD

YURKOV

ZSIVOCZKYMcMULLEN

MARTINEAU

HERNU

BARRAS

NOOL

BOURGUIGNON

SebrleClay

Karpov

MaceyWarners

Zsivoczky

Hernu

BernardSchwarzl

PogorelovSchoenbeck

Barras

-2-1012 -2.5 0.02.5

Dim1 (41.2%)

Dim2 (18.4%)

cos2 0.25 0.50 0.75

Individuals - PCA

Sepal.LengthSepal.Width

Petal.LengthPetal.Width

-2-10123 -2 02

Dim1 (73%)

Dim2 (22.9%)

Clusters

a a petal sepal

Species

setosa versicolor virginica

PCA - Biplot

Laundry

Main_meal

DinnerBreakfeast

Tidying

DishesShoppingOfficialDriving

Finances

InsuranceRepairs

Holidays

Wife

AlternatingHusband

Jointly

-1.5-1.0-0.50.00.5 -1.0 -0.50.0 0.51.0 1.5

Dim1 (48.7%)

Dim2 (39.9%)

CA - Biplot

2EL 1CHA 1FON 1VAU 1DAM 2BOU 1BOI 3EL DOM1

1TUR4EL PER12DAM

1POY

1ING1BEN

2BEA 1ROC 2ING

T1 T2

Bourgueuil

Chinon

Saumur

2EL 1CHA 1FON 1VAU 1DAM 2BOU 1BOI 3EL DOM1

1TUR4EL

PER1 2DAM 1POY 1ING 1BEN 2BEA 1ROC 2ING T1 T2 Env1

Env2Env4

Reference

LabelSoil

-6 -4 -2 02-6 -4 -2 02-3-2-1012

Dim1 (43.9%)

Dim2 (16.9%)

FAMD factor map

South CarolinaMississippiNorth CarolinaLouisianaGeorgiaAlabamaTennessee ArizonaNew YorkTexasIllinoisFloridaMarylandNew MexicoMichiganAlaskaColoradoCaliforniaNevada

West VirginiaSouth DakotaNorth DakotaVermontMontanaIdahoNebraskaWisconsinMinnesotaMaineNew HampshireIowaRhode IslandMassachusettsNew JerseyHawaiiUtahConnecticutPennsylvaniaKentuckyArkansasDelawareWyomingVirginiaOhioKansasIndianaOklahomaMissouriWashingtonOregon

-1012

Height

Cluster Dendrogram

PC1PC2

-4004080 -10 -5 0 510 x y

Plot 1A

-40-2002040 -80 -40 040 PC1 PC2

Plot 1B

-3-2-10123 -2 -1 0 1 2 x y

Low redundancy

-404 -2 0 2 x y

High redundancy

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