Description Usage Arguments Details Value Author(s) References See Also Examples

Provides a plot of SD cluster validation index values for different numbers of k-means clusters for a common underlying dataset. The number of clusters that has the lowest value of the SD index represents the "best" solution under the criteria used to construct the SD index.

1 | ```
SDIndex(x, minClust, maxClust, iter.max=10, num.seeds=10)
``` |

`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). |

`minClust` |
The minimum number of clusters to be considered for a solution. |

`maxClust` |
The maximum number of clusters to be considered for a solution. |

`iter.max` |
The maximum number of iterations allowed for a solution. |

`num.seeds` |
The number of different starting random seeds to use for a solution with a given number of clusters. |

The SD index corresponds to the weighted sum of the average "scattering" of points within clusters and the inverse of the total seperation between clusters. The average scattering measure is based on the average sum of the squared differences between a clusters centroid all the points in a cluster, while total seperation is measured by the sum of the squared distance between cluster centroids. A solution with a low average scattering and a low value of the inverse total seperation is considered to be better than a solution with higher levels of these two measures.

The function returns invisibly. Its benefit is the side effect plot produced.

Dan Putler

M. Haldiki, Y. Batistakis, M. Vazirgiannis (2001), On Clustering Validation Techniques,
*Journal of Intelligent Information Systems,* 17:2/3.

1 2 3 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.