Clustering Library (ClusteringLib)


ShapeIndependentClustering
to make shape independent clustering
 (Kohei Arai, Ali Ridho Barakbah, "Method for shape independent clustering in case of numerical clustering together with condensed clustering", The 8th World Multi-Conference on Systemics, Cybernetics and Informatics (SCI 2004), July 18-21, 2004, Orlando, Florida)



FUNCTIONS

int[] - ShapeIndependentClustering(int[][] data)
int[] - ShapeIndependentClustering(double[][] data)
int[] - ShapeIndependentClustering(int[][] data, double threshold)
int[] - ShapeIndependentClustering(double[][] data, double threshold)



EXAMPLES

Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read U-shape dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("u_shape");

// Shape Independent Clustering with default threshold=1.5
int[] clusters=clib.ShapeIndependentClustering(data);

vlib.setDraw(data, clusters);
Output


Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read Inter-related dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("interrelated_shape");

// Shape Independent Clustering with threshold=2
int[] clusters=clib.ShapeIndependentClustering(data, 2);

vlib.setDraw(data, clusters);
Output


Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read Contiguous dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("contiguous_shape");

// Shape Independent Clustering with default threshold=1.5
int[] clusters=clib.ShapeIndependentClustering(data);

vlib.setDraw(data, clusters);
Output


Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read S-shape dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("s_shape");

// Shape Independent Clustering with default threshold=1.5
int[] clusters=clib.ShapeIndependentClustering(data);

vlib.setDraw(data, clusters);
Output


Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read Circular nested dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("circular_nested_shape");

// Shape Independent Clustering with default threshold=1.5
int[] clusters=clib.ShapeIndependentClustering(data);

vlib.setDraw(data, clusters);
Output


Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read Diamond dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("diamond_shape");

// Shape Independent Clustering with default threshold=1.5
int[] clusters=clib.ShapeIndependentClustering(data);

vlib.setDraw(data, clusters);
Output


Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read Complex inter-related dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("complex_interrelated_shape");

// Shape Independent Clustering with threshold=1.25
int[] clusters=clib.ShapeIndependentClustering(data, 1.25);

vlib.setDraw(data, clusters);
Output


Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read Normal dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("normal");

// Shape Independent Clustering with default threshold=1.5
int[] clusters=clib.ShapeIndependentClustering(data);

vlib.setDraw(data, clusters);
Output


Example VectorLib vlib=new VectorLib();
ClusteringLib clib=new ClusteringLib();

// Read Random dataset for Shape Independent Clustering
int[][] data=vlib.readDST_2D_int("random");

// Shape Independent Clustering with threshold=1
int[] clusters=clib.ShapeIndependentClustering(data, 1);

vlib.setDraw(data, clusters);
Output