K Means

K Means Clustering

K Means Clustering is an unsupervised learning algorithm that will attempt to group similar clusters together in your data.

• So what does a typical clustering problem look like?
• Cluster Similar Documents
• Cluster Customers based on features
• Market Segmentation
• Identify similar physcial groups

The overall goal is to divide data into distinct groups such that obervations within each group are similar

Dividing Groups

Example

Exploring Data

library(ISLR)
print(head(iris))

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

# install.packages('ggplot2')
library(ggplot2)
pl <- ggplot(iris, aes(Petal.Length, Petal.Width, color= Species))
print(pl + geom_point(size =3))

set.seed(101)
irisCluster <- kmeans(iris[, 1:4], 3, nstart = 20)
print(irisCluster)

## K-means clustering with 3 clusters of sizes 62, 50, 38
## 
## Cluster means:
##   Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1     5.901613    2.748387     4.393548    1.433871
## 2     5.006000    3.428000     1.462000    0.246000
## 3     6.850000    3.073684     5.742105    2.071053
## 
## Clustering vector:
##   [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
##  [36] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [71] 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 3 3 3
## [106] 3 1 3 3 3 3 3 3 1 1 3 3 3 3 1 3 1 3 1 3 3 1 1 3 3 3 3 3 1 3 3 3 3 1 3
## [141] 3 3 1 3 3 3 1 3 3 1
## 
## Within cluster sum of squares by cluster:
## [1] 39.82097 15.15100 23.87947
##  (between_SS / total_SS =  88.4 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"    
## [5] "tot.withinss" "betweenss"    "size"         "iter"        
## [9] "ifault"

table(irisCluster$cluster, iris$Species)

##    
##     setosa versicolor virginica
##   1      0         48        14
##   2     50          0         0
##   3      0          2        36

library(cluster)
# help("clusplot")
# help("kmeans")
clusplot(iris,irisCluster$cluster, color = T,shade = T, labels = 0, lines =0 )