java-clustering

Package provides java implementation of various clustering algorithms

Features

• Hierarchical Clustering
• KMeans Clustering
• DBSCAN
• Single Linkage Clustering

Install

Add the following dependency to your POM file:

<dependency>
  <groupId>com.github.chen0040</groupId>
  <artifactId>java-clustering</artifactId>
  <version>1.0.3</version>
</dependency>

Spatial Segmentation using Hierarchical Clustering

The following sample code shows how to use hierarchical clustering to separate two clusters:

DataQuery.DataFrameQueryBuilder schema = DataQuery.blank()
      .newInput("c1")
      .newInput("c2")
      .newOutput("designed")
      .end();

Sampler.DataSampleBuilder negativeSampler = new Sampler()
      .forColumn("c1").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
      .forColumn("c2").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
      .forColumn("designed").generate((name, index) -> 0.0)
      .end();

Sampler.DataSampleBuilder positiveSampler = new Sampler()
      .forColumn("c1").generate((name, index) -> rand(-4, -2))
      .forColumn("c2").generate((name, index) -> rand(-2, -4))
      .forColumn("designed").generate((name, index) -> 1.0)
      .end();

DataFrame data = schema.build();

data = negativeSampler.sample(data, 50);
data = positiveSampler.sample(data, 50);

System.out.println(data.head(10));

HierarchicalClustering algorithm = new HierarchicalClustering();
algorithm.setLinkage(linkageCriterion);
algorithm.setClusterCount(2);

DataFrame learnedData = algorithm.fitAndTransform(data);

for(int i = 0; i < learnedData.rowCount(); ++i){
 DataRow tuple = learnedData.row(i);
 String clusterId = tuple.getCategoricalTargetCell("cluster");
 System.out.println("learned: " + clusterId +"\tknown: "+tuple.target());
}

Spatial Segmentation using EM Clustering

The following sample code shows how to use EM clustering to separate two clusters:

DataQuery.DataFrameQueryBuilder schema = DataQuery.blank()
      .newInput("c1")
      .newInput("c2")
      .newOutput("designed")
      .end();

Sampler.DataSampleBuilder negativeSampler = new Sampler()
      .forColumn("c1").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
      .forColumn("c2").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
      .forColumn("designed").generate((name, index) -> 0.0)
      .end();

Sampler.DataSampleBuilder positiveSampler = new Sampler()
      .forColumn("c1").generate((name, index) -> rand(-4, -2))
      .forColumn("c2").generate((name, index) -> rand(-2, -4))
      .forColumn("designed").generate((name, index) -> 1.0)
      .end();

DataFrame data = schema.build();

data = negativeSampler.sample(data, 50);
data = positiveSampler.sample(data, 50);

System.out.println(data.head(10));

EMClustering algorithm = new EMClustering();
algorithm.setSigma0(1.5);
algorithm.setClusterCount(2);

DataFrame learnedData = algorithm.fitAndTransform(data);

for(int i = 0; i < learnedData.rowCount(); ++i){
 DataRow tuple = learnedData.row(i);
 String clusterId = tuple.getCategoricalTargetCell("cluster");
 System.out.println("learned: " + clusterId +"\tknown: "+tuple.target());
}

Spatial Segmentation using Single Linkage Clustering

The following sample code shows how to use single linkage clustering to separate two clusters:

DataQuery.DataFrameQueryBuilder schema = DataQuery.blank()
      .newInput("c1")
      .newInput("c2")
      .newOutput("designed")
      .end();

Sampler.DataSampleBuilder negativeSampler = new Sampler()
      .forColumn("c1").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
      .forColumn("c2").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
      .forColumn("designed").generate((name, index) -> 0.0)
      .end();

Sampler.DataSampleBuilder positiveSampler = new Sampler()
      .forColumn("c1").generate((name, index) -> rand(-4, -2))
      .forColumn("c2").generate((name, index) -> rand(-2, -4))
      .forColumn("designed").generate((name, index) -> 1.0)
      .end();

DataFrame data = schema.build();

data = negativeSampler.sample(data, 50);
data = positiveSampler.sample(data, 50);

System.out.println(data.head(10));

SingleLinkageClustering algorithm = new SingleLinkageClustering();
algorithm.setClusterCount(2);

DataFrame learnedData = algorithm.fitAndTransform(data);

for(int i = 0; i < learnedData.rowCount(); ++i){
 DataRow tuple = learnedData.row(i);
 String clusterId = tuple.getCategoricalTargetCell("cluster");
 System.out.println("learned: " + clusterId +"\tknown: "+tuple.target());
}

Spatial Segmentation using DBSCAN

The following sample code shows how to use DBSCAN to perform clustering:

DataQuery.DataFrameQueryBuilder schema = DataQuery.blank()
      .newInput("c1")
      .newInput("c2")
      .newOutput("designed")
      .end();

Sampler.DataSampleBuilder negativeSampler = new Sampler()
      .forColumn("c1").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
      .forColumn("c2").generate((name, index) -> randn() * 0.3 + (index % 2 == 0 ? 2 : 4))
      .forColumn("designed").generate((name, index) -> 0.0)
      .end();

Sampler.DataSampleBuilder positiveSampler = new Sampler()
      .forColumn("c1").generate((name, index) -> rand(-4, -2))
      .forColumn("c2").generate((name, index) -> rand(-2, -4))
      .forColumn("designed").generate((name, index) -> 1.0)
      .end();

DataFrame data = schema.build();

data = negativeSampler.sample(data, 200);
data = positiveSampler.sample(data, 200);

System.out.println(data.head(10));

DBSCAN algorithm = new DBSCAN();
algorithm.setEpsilon(0.5);

DataFrame learnedData = algorithm.fitAndTransform(data);

for(int i = 0; i < learnedData.rowCount(); ++i){
 DataRow tuple = learnedData.row(i);
 String clusterId = tuple.getCategoricalTargetCell("cluster");
 System.out.println("learned: " + clusterId +"\tknown: "+tuple.target());
}

Image Segmentation (Clustering) using KMeans

The following sample code shows how to use FuzzyART to perform image segmentation:

BufferedImage img= ImageIO.read(FileUtils.getResource("1.jpg"));

DataFrame dataFrame = ImageDataFrameFactory.dataFrame(img);

KMeans cluster = new KMeans();
DataFrame learnedData = cluster.fitAndTransform(dataFrame);

for(int i=0; i <learnedData.rowCount(); ++i) {
 ImageDataRow row = (ImageDataRow)learnedData.row(i);
 int x = row.getPixelX();
 int y = row.getPixelY();
 String clusterId = row.getCategoricalTargetCell("cluster");
 System.out.println("cluster id for pixel (" + x + "," + y + ") is " + clusterId);
}

The segmented image can be generated using the trained KMeans from above as illustrated by the following sample code:

List<Integer> classColors = new ArrayList<Integer>();
for(int i=0; i < 5; ++i){
 for(int j=0; j < 5; ++j){
    classColors.add(ImageDataFrameFactory.get_rgb(255, rand.nextInt(255), rand.nextInt(255), rand.nextInt(255)));
 }
}

BufferedImage segmented_image = new BufferedImage(img.getWidth(), img.getHeight(), img.getType());
for(int x=0; x < img.getWidth(); x++)
{
 for(int y=0; y < img.getHeight(); y++)
 {
    int rgb = img.getRGB(x, y);

    DataRow tuple = ImageDataFrameFactory.getPixelTuple(x, y, rgb);

    int clusterIndex = cluster.transform(tuple);

    rgb = classColors.get(clusterIndex % classColors.size());

    segmented_image.setRGB(x, y, rgb);
 }
}