ITS836 Cumberlands Types of Iris in Hawaii Clustering Methods Project Deliverable should be a powerpoint or Word, Use existing homework template With your

ITS836 Cumberlands Types of Iris in Hawaii Clustering Methods Project Deliverable should be a powerpoint or Word, Use existing homework template With your name, ID and date. School of Computer &
Information Sciences
ITS 836 Data Science and Big Data Analytics
1
Lecture 04: Clustering – Homework
Homework 1: Review the Iris Data set perform
clustering via “kmeans” (see next slides)
Homework 2: Perform hierarchical clustering on
the iris data set: https://cran.rproject.org/web/packages/dendextend/vignett
es/Cluster_Analysis.html
Homework 3: Use the data set USArrests, to
cluster the US States according to
https://uc-r.github.io/kmeans_clustering
Deliverable should be a powerpoint or
Homework 4: Review Section 4_2R Exercise
Word, Use existing homework template
Homework 5 Clustering on a data set
With your name, ID and date.
Homework 6: Continue R for Datascience
ITS 836
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exercises
Iris Dataset Source
Goal: Predict the types of iris in Hawaii
R.A. Fisher, 1936
• Attributes: sepal length, sepal width, petal length, petal width
– All flowers contain a sepal and a petal
– For the iris flowers three categories (Versicolor, Setosa, Virginica) different measurements
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View Iris Data Set
Iris data comes with R install
str(iris)
‘data.frame’:
150 obs. of 5 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 …
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 …
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 …
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 …
$ Species : Factor w/ 3 levels “setosa”,”versicolor”,..: 1 1 1 1 1 1 1 1 1 1 …
Species attribute: division of the species of flowers is 50-50-50.
table(iris$Species)
setosa versicolor virginica
50
50
50
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Visualize iris data
library(ggplot2)
ggplot(data = iris, aes(x = Sepal.Length, y =
Sepal.Width, col = Species)) + geom_point()
• Observe:
– High correlation between
the sepal length and the
sepal width of the Setosa
iris
– Lesser correlation for
Virginica and Versicolor
• the data points are more
spread out over the graph
and don’t form a cluster
like you can see in the case
of the Setosa flowers.
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Visualize iris data
ggplot(data = iris, aes(x = Petal.Length, y =
Petal.Width, col = Species)) + geom_point()
• Positive correlation:
– between the petal
length and the petal
width for all species
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correlations
library(GGally)
ggpairs(iris)
• As shown correlation
between Petal Width &:
– Petal Length (0.963)
– Sepal Length (0.818)
• And b/w Petal Length
– Sepal Length (0.872)
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Lecture 4 Homework 1:
Clustering with k-means
head(iris)
#remove last column
iris_2
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