/* file: DBSCANDenseBatch.java */
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 //  Content:
 //     Java example of dense DBSCAN clustering in the batch processing mode

package com.intel.daal.examples.dbscan;

import com.intel.daal.algorithms.dbscan.*;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;

class DBSCANDenseBatch {
    /* Input data set parameters */
    private static final String dataset       = "../data/batch/dbscan_dense.csv";

    /* DBSCAN algorithm parameters */
    private static final double epsilon = 0.02;
    private static final long   minObservations = 180;

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
        /* Retrieve the input data */
        FileDataSource dataSource = new FileDataSource(context, dataset,
        NumericTable input = dataSource.getNumericTable();

        /* Create an algorithm for DBSCAN clustering */
        Batch algorithm = new Batch(context, Float.class, Method.defaultDense, epsilon, minObservations);

        /* Set an input object for the algorithm */
        algorithm.input.set(InputId.data, input);

        /* Clusterize the data */
        Result result = algorithm.compute();

        /* Print the results */
        Service.printNumericTable("Number of clusters:", result.get(ResultId.nClusters));
        Service.printNumericTable("Assignments of first 20 observations:", result.get(ResultId.assignments), 20);

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