Clustering Library (ClusteringLib)


Clustering
to cluster data



FUNCTIONS

int[] - Clustering(String algorithm, int[][] data, int k)
int[] - Clustering(String algorithm, double[][] data, int k)
int[] - Clustering(String algorithm, int[][] data, int k, double[][] centroids)
int[] - Clustering(String algorithm, double[][] data, int k, double[][] centroids)

Note:
Algorithm can be kmeans, single, centroid, complete, or average linkage.



EXAMPLES

Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();
int[][] data={{20, 30}, {30, 20}, {30, 30}, {70, 70}, {80, 80}};
int[] clusters=clib.Clustering("centroid", data, 2);
vlib.view("Clusters", clusters);
Output clusters =
0      0      0      1      1






EXAMPLES

Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();
int[][] data={{20, 30}, {30, 20}, {30, 30}, {70, 70}, {80, 80}};
int k=2;
double[][] centroids=clib.initCentroid(data, k);
int[] clusters=clib.Clustering("kmeans", data, k, centroids);
vlib.view("Centroids", centroids);
vlib.view("Clusters", clusters);
Output Centroids =
24.0      22.0
71.0      66.0

Clusters =
0      0      0      1      1