JarCastingjava-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);
}
}