Clustering

In [15]:

    

set.seed(42)
somex <- rnorm(4) # Abscissas dos centroides
somey <- rnorm(4)
X <- numeric(104) # 4 para os centroides e 100 para as amostras
Y <- numeric(104) # 4 para os centroides e 100 para as amostras
j <- 1
for (i in 1:4) { # Para cada centroide, vamos gerar alguns pontos
    X[j] <- somex[i]
    Y[j] <- somey[i]
    j = j + 1
    for (z in 1:25) {
        xz <- 0.12 * rnorm(1) + somex[i]
        yz <- 0.12 * rnorm(1) + somey[i]
        X[j] <- xz
        Y[j] <- yz
        j = j + 1
    }
}


    

In [23]:

    

plot(X,Y)
points(somex,somey, pch = 8, col = 2, cex = 9)


    

In [34]:

    

df <- data.frame(x = X, y = Y)
modelo <- kmeans(df,4)


    

In [29]:

    

modelo


    

    






K-means clustering with 4 clusters of sizes 15, 52, 11, 26

Cluster means:
           x           y
1 -0.6624353 -0.09825517
2  0.9937956  0.15575292
3 -0.4665211 -0.03672141
4  0.3367062  1.47904936

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 1 1 1 3 1 3 3 1 3 1 3
 [38] 1 3 1 1 1 3 3 3 1 1 3 3 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 [75] 4 4 4 4 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

Within cluster sum of squares by cluster:
[1]  0.2520460 11.9370096  0.1052387  0.5583075
 (between_SS / total_SS =  86.5 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"      

In [31]:

    

modelo$cluster


    

    






	
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In [32]:

    

modelo$centers


    

    






x
y

	
1
-0.6624353 
-0.09825517
	
2
 0.9937956 
 0.15575292
	
3
-0.4665211 
-0.03672141
	
4
 0.3367062 
 1.47904936

In [36]:

    

plot(X,Y,col = modelo$cluster, pch = modelo$cluster)
points(somex,somey, pch = 8, col = 2, cex = 9)


    

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