Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

DBSCANDenseBatch.java

/* 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,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
dataSource.loadDataBlock();
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);
context.dispose();
}
}

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