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Last data update: 2014.03.03

R: K-Means Clustering Using Multiple Random Seeds

KMeans

R Documentation

K-Means Clustering Using Multiple Random Seeds

Description

Finds a number of k-means clusting solutions using R's kmeans function, and selects as the final solution the one that has the minimum total within-cluster sum of squared distances.

Usage

KMeans(x, centers, iter.max=10, num.seeds=10)

Arguments

`x`	A numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a dataframe with all numeric columns).
`centers`	The number of clusters in the solution.
`iter.max`	The maximum number of iterations allowed.
`num.seeds`	The number of different starting random seeds to use. Each random seed results in a different k-means solution.

Value

A list with components:

`cluster`	A vector of integers indicating the cluster to which each point is allocated.
`centers`	A matrix of cluster centres (centroids).
`withinss`	The within-cluster sum of squares for each cluster.
`tot.withinss`	The within-cluster sum of squares summed across clusters.
`betweenss`	The between-cluster sum of squared distances.
`size`	The number of points in each cluster.

Author(s)

Dan Putler

Examples

  data(USArrests)
  KMeans(USArrests, centers=3, iter.max=5, num.seeds=5)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RcmdrMisc)
Loading required package: car
Loading required package: sandwich
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RcmdrMisc/KMeans.Rd_%03d_medium.png", width=480, height=480)
> ### Name: KMeans
> ### Title: K-Means Clustering Using Multiple Random Seeds
> ### Aliases: KMeans
> ### Keywords: misc
> 
> ### ** Examples
> 
>   data(USArrests)
>   KMeans(USArrests, centers=3, iter.max=5, num.seeds=5)
K-means clustering with 3 clusters of sizes 20, 14, 16

Cluster means:
     Murder  Assault UrbanPop     Rape
1  4.270000  87.5500 59.75000 14.39000
2  8.214286 173.2857 70.64286 22.84286
3 11.812500 272.5625 68.31250 28.37500

Clustering vector:
       Alabama         Alaska        Arizona       Arkansas     California 
             3              3              3              2              3 
      Colorado    Connecticut       Delaware        Florida        Georgia 
             2              1              3              3              2 
        Hawaii          Idaho       Illinois        Indiana           Iowa 
             1              1              3              1              1 
        Kansas       Kentucky      Louisiana          Maine       Maryland 
             1              1              3              1              3 
 Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
             2              3              1              3              2 
       Montana       Nebraska         Nevada  New Hampshire     New Jersey 
             1              1              3              1              2 
    New Mexico       New York North Carolina   North Dakota           Ohio 
             3              3              3              1              1 
      Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
             2              2              1              2              3 
  South Dakota      Tennessee          Texas           Utah        Vermont 
             1              2              2              1              1 
      Virginia     Washington  West Virginia      Wisconsin        Wyoming 
             2              2              1              1              2 

Within cluster sum of squares by cluster:
[1] 19263.760  9136.643 19563.863
 (between_SS / total_SS =  86.5 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"      
> 
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> 
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> dev.off()
null device 
          1 
>