Help documentation-Translation-statistics toolbox-exploratory Data analysis-cluster analysis-hierarchical Clustering (cluster,clusterdata) ( 2)

Example

Generating hierarchical cluster trees from sample data

This example shows the use of sample data to generate hierarchical clusters, and a 3-D scatter plot to present the cluster.

Produces the sample data matrix, where the random number is generated by the standard uniform distribution (U (0,1)).

RNG (' Default '); % for Reproducibilty

X = [Gallery (' Uniformdata ', [10 3],12);

Gallery (' Uniformdata ', [10 3],13) +1.2;

Gallery (' Uniformdata ', [10 3],14) +2.5];

Calculates the distance between elements and generates a hierarchical number of clusters from the sample data. Lists all the elements in the second cluster.

T = Clusterdata (X, ' Maxclust ', 3);

Find (T = = 2)

Ans =

11 12 13 14 15 16 17 18 19 20

Plot the data into a graphic, where each category corresponds to a different color.

Scatter3 (X (:, 1), X (:, 2), X (:, 3), 100,t, ' filled ')

Generating hierarchical cluster trees using Ward's linkage

This example shows the use of Ward's linkage to generate hierarchical clustering numbers, and a 3-D scatter plot to present the cluster.

Produces a sample data matrix of 20,000 rows and 3 columns, where the random number is generated by the standard uniform distribution (U (0,1)).

RNG (' Default '); % for Reproducibilty

X = rand (20000,3);

Use Ward's linkage to generate hierarchical clustering trees. Set ' savememory ' to ' on ' to construct the cluster but not to calculate the distance matrix.

c = Clusterdata (X, ' linkage ', ' ward ', ' savememory ', ' on ', ' Maxclust ', 4);

Plot the data into a graphic, where each category corresponds to a different color.

Scatter3 (X (:, 1), X (:, 2), X (:, 3), 10,c)

Help documentation-Translation-statistics toolbox-exploratory Data analysis-cluster analysis-hierarchical Clustering (cluster,clusterdata) ( 2)