Interactive Clustering Method

Parameters | Genetics | Interactive Clustering Method

Interactive Clustering Method

Use the drop-down menu to specify the method to use for hierarchical clustering (via the JMP Clustering Platform) when using Interactive compression.

Note : This parameter is available only when Interactive has been selected as the Compression Method .

Clustering methods are described in the following table:

Clustering Method

Description

Average

Choose this method to set the distance between clusters to the average distance between pairs of observations .

This method tends to join clusters with small variances and is biased toward producing clusters with the same variance. ¹

Centroid

Choose this method to set the distance between clusters to the squared Euclidean Distance between the mean of each cluster. ²

This method is more robust than other clustering methods, but is more difficult to interpret in terms of recombination frequency behavior between groups.

Ward

Choose this method to set the distance between clusters to the ANOVA sum of squares between the two clusters summed over all the variables . At each generation, two clusters from the previous generation are merged to reduce the within-cluster sum of squares over all partitions. The sums of squares are easier to interpret when they are divided by the total sum of squares to give the proportions of variance (squared semipartial correlations).

This method joins clusters to maximize the likelihood at each level of the hierarchy under the assumptions of multivariate normal mixtures, spherical covariance matrices, and equal sampling probabilities.

This method tends to join clusters with a small number of observations and is biased toward producing clusters with approximately the same number of observations. It is also very sensitive to outliers. 2

Single

Choose this method to set the distance between two clusters to the minimum distance between an observation in one cluster and an observation in the other cluster.

This method is often the method used in other linkage grouping software. However, because it can suffer from chaining together spuriously linked markers, it is biased towards forming imbalanced groups.

Complete

Choose this method to set the distance between clusters to the maximum distance between an observation in one cluster and an observation in the other . 2

This method is biased toward producing clusters of equivalent diameters and can be distorted by even moderate outliers.

1

Sokal, R.R., and C.D. Michener. (1958) A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin 38: 1409-1438.

2

Milligan, G.W. (1980) An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika 45: 325-342.

To Specify a Clustering Method:

Specify Interactive as the Compression Method .

Make a selection using the drop-down menu.

Clustering Method	Description
Average	Choose this method to set the distance between clusters to the average distance between pairs of observations . This method tends to join clusters with small variances and is biased toward producing clusters with the same variance. ¹
Centroid	Choose this method to set the distance between clusters to the squared Euclidean Distance between the mean of each cluster. ² This method is more robust than other clustering methods, but is more difficult to interpret in terms of recombination frequency behavior between groups.
Ward	Choose this method to set the distance between clusters to the ANOVA sum of squares between the two clusters summed over all the variables . At each generation, two clusters from the previous generation are merged to reduce the within-cluster sum of squares over all partitions. The sums of squares are easier to interpret when they are divided by the total sum of squares to give the proportions of variance (squared semipartial correlations). This method joins clusters to maximize the likelihood at each level of the hierarchy under the assumptions of multivariate normal mixtures, spherical covariance matrices, and equal sampling probabilities. This method tends to join clusters with a small number of observations and is biased toward producing clusters with approximately the same number of observations. It is also very sensitive to outliers. 2
Single	Choose this method to set the distance between two clusters to the minimum distance between an observation in one cluster and an observation in the other cluster. This method is often the method used in other linkage grouping software. However, because it can suffer from chaining together spuriously linked markers, it is biased towards forming imbalanced groups.
Complete	Choose this method to set the distance between clusters to the maximum distance between an observation in one cluster and an observation in the other . 2 This method is biased toward producing clusters of equivalent diameters and can be distorted by even moderate outliers.