Iris_MultipleClustering

set.seed(1234)
randDat<-matrix(rnorm(50), nrow=5)
x<-seq(-10, 10, by =.1)
y<-dnorm(x, mean=2.5, sd=0.5)

plot(x,y)

dist(randDat) #Euclidian Distance default

##          1        2        3        4
## 2 4.261667                           
## 3 4.038030 2.060117                  
## 4 3.456732 3.726399 4.037978         
## 5 5.307253 4.415046 4.111230 4.814393

dist(randDat, method="manhattan")

##           1         2         3         4
## 2 11.382197                              
## 3 10.016795  4.536827                    
## 4  9.887932  8.845512  8.829131          
## 5 14.683770 10.617871  9.091241 11.362705

dist(randDat, method="minkowski", p=4)

##          1        2        3        4
## 2 2.899494                           
## 3 2.875467 1.653824                  
## 4 2.208297 2.814135 3.453336         
## 5 3.488531 3.192217 3.398721 3.643788

## Iris Dataset analysis with multiple clustering ##algorithms

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