Examples of AlgoHierarchicalClustering

image.processing.algorithms.clustering.kmeans.AlgoHierarchicalClustering
This is a Hierarchical Clustering with a constant "threshold" that indicate the maximal distance between two clusters to group them. The algorithm stops when no cluster can be merged. The distance between two clusters is calculated as the distance between the medians of the two clusters. @author Philippe Fournier-Viger

Examples of image.processing.algorithms.clustering.kmeans.AlgoHierarchicalClustering

    String input = fileToPath("current_colour_data.txt");
    String output = ".//output_colour.txt";
    int maxdistance = _k;
    
    // Apply the algorithm
    AlgoHierarchicalClustering algo = new AlgoHierarchicalClustering();  // we request 3 clusters
    algo.runAlgorithm(input, maxdistance);
    algo.printStatistics();
    algo.saveToColFile(output);


  }

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Examples of image.processing.algorithms.clustering.kmeans.AlgoHierarchicalClustering

    String input = fileToPath("current_feature_data.txt");
    String output = ".//output_feature.txt";
    int maxdistance = _k;
    
    // Apply the algorithm
    AlgoHierarchicalClustering algo = new AlgoHierarchicalClustering();  // we request 3 clusters
    algo.runAlgorithm(input, maxdistance);
    algo.printStatistics();
    algo.saveToFeatFile(output);


  }

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Examples of image.processing.algorithms.clustering.kmeans.AlgoHierarchicalClustering

    String input = fileToPath("current_combined_data.txt");
    String output = ".//output_combined.txt";
    double maxdistance = _k;
    
    // Apply the algorithm
    AlgoHierarchicalClustering algo = new AlgoHierarchicalClustering();  // we request 3 clusters
    algo.runAlgorithm(input, maxdistance);
    algo.printStatistics();
    algo.saveToCombFile(output);


  }

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