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java-clustering\nPackage provides java implementation of various clustering algorithms\n\n[![Build Status](https://travis-ci.org/chen0040/java-clustering.svg?branch=master)](https://travis-ci.org/chen0040/java-clustering) [![Coverage Status](https://coveralls.io/repos/github/chen0040/java-clustering/badge.svg?branch=master)](https://coveralls.io/github/chen0040/java-clustering?branch=master)\n \n# Features\n\n* Hierarchical Clustering\n* KMeans Clustering\n* DBSCAN\n* Single Linkage Clustering\n \n# Install\n\nAdd the following dependency to your POM file:\n\n```xml\n\u003cdependency\u003e\n  \u003cgroupId\u003ecom.github.chen0040\u003c/groupId\u003e\n  \u003cartifactId\u003ejava-clustering\u003c/artifactId\u003e\n  \u003cversion\u003e1.0.3\u003c/version\u003e\n\u003c/dependency\u003e\n```\n\n### Spatial Segmentation using Hierarchical Clustering\n\nThe following sample code shows how to use hierarchical clustering to separate two clusters:\n\n```java\nDataQuery.DataFrameQueryBuilder schema = DataQuery.blank()\n      .newInput(\"c1\")\n      .newInput(\"c2\")\n      .newOutput(\"designed\")\n      .end();\n\nSampler.DataSampleBuilder negativeSampler = new Sampler()\n      .forColumn(\"c1\").generate((name, index) -\u003e randn() * 0.3 + (index % 2 == 0 ? 2 : 4))\n      .forColumn(\"c2\").generate((name, index) -\u003e randn() * 0.3 + (index % 2 == 0 ? 2 : 4))\n      .forColumn(\"designed\").generate((name, index) -\u003e 0.0)\n      .end();\n\nSampler.DataSampleBuilder positiveSampler = new Sampler()\n      .forColumn(\"c1\").generate((name, index) -\u003e rand(-4, -2))\n      .forColumn(\"c2\").generate((name, index) -\u003e rand(-2, -4))\n      .forColumn(\"designed\").generate((name, index) -\u003e 1.0)\n      .end();\n\nDataFrame data = schema.build();\n\ndata = negativeSampler.sample(data, 50);\ndata = positiveSampler.sample(data, 50);\n\nSystem.out.println(data.head(10));\n\nHierarchicalClustering algorithm = new HierarchicalClustering();\nalgorithm.setLinkage(linkageCriterion);\nalgorithm.setClusterCount(2);\n\nDataFrame learnedData = algorithm.fitAndTransform(data);\n\nfor(int i = 0; i \u003c learnedData.rowCount(); ++i){\n DataRow tuple = learnedData.row(i);\n String clusterId = tuple.getCategoricalTargetCell(\"cluster\");\n System.out.println(\"learned: \" + clusterId +\"\\tknown: \"+tuple.target());\n}\n```\n\n### Spatial Segmentation using EM Clustering\n\nThe following sample code shows how to use EM clustering to separate two clusters:\n\n```java\nDataQuery.DataFrameQueryBuilder schema = DataQuery.blank()\n      .newInput(\"c1\")\n      .newInput(\"c2\")\n      .newOutput(\"designed\")\n      .end();\n\nSampler.DataSampleBuilder negativeSampler = new Sampler()\n      .forColumn(\"c1\").generate((name, index) -\u003e randn() * 0.3 + (index % 2 == 0 ? 2 : 4))\n      .forColumn(\"c2\").generate((name, index) -\u003e randn() * 0.3 + (index % 2 == 0 ? 2 : 4))\n      .forColumn(\"designed\").generate((name, index) -\u003e 0.0)\n      .end();\n\nSampler.DataSampleBuilder positiveSampler = new Sampler()\n      .forColumn(\"c1\").generate((name, index) -\u003e rand(-4, -2))\n      .forColumn(\"c2\").generate((name, index) -\u003e rand(-2, -4))\n      .forColumn(\"designed\").generate((name, index) -\u003e 1.0)\n      .end();\n\nDataFrame data = schema.build();\n\ndata = negativeSampler.sample(data, 50);\ndata = positiveSampler.sample(data, 50);\n\nSystem.out.println(data.head(10));\n\nEMClustering algorithm = new EMClustering();\nalgorithm.setSigma0(1.5);\nalgorithm.setClusterCount(2);\n\nDataFrame learnedData = algorithm.fitAndTransform(data);\n\nfor(int i = 0; i \u003c learnedData.rowCount(); ++i){\n DataRow tuple = learnedData.row(i);\n String clusterId = tuple.getCategoricalTargetCell(\"cluster\");\n System.out.println(\"learned: \" + clusterId +\"\\tknown: \"+tuple.target());\n}\n```\n\n### Spatial Segmentation using Single Linkage Clustering\n\nThe following sample code shows how to use single linkage clustering to separate two clusters:\n\n```java\nDataQuery.DataFrameQueryBuilder schema = DataQuery.blank()\n      .newInput(\"c1\")\n      .newInput(\"c2\")\n      .newOutput(\"designed\")\n      .end();\n\nSampler.DataSampleBuilder negativeSampler = new Sampler()\n      .forColumn(\"c1\").generate((name, index) -\u003e randn() * 0.3 + (index % 2 == 0 ? 2 : 4))\n      .forColumn(\"c2\").generate((name, index) -\u003e randn() * 0.3 + (index % 2 == 0 ? 2 : 4))\n      .forColumn(\"designed\").generate((name, index) -\u003e 0.0)\n      .end();\n\nSampler.DataSampleBuilder positiveSampler = new Sampler()\n      .forColumn(\"c1\").generate((name, index) -\u003e rand(-4, -2))\n      .forColumn(\"c2\").generate((name, index) -\u003e rand(-2, -4))\n      .forColumn(\"designed\").generate((name, index) -\u003e 1.0)\n      .end();\n\nDataFrame data = schema.build();\n\ndata = negativeSampler.sample(data, 50);\ndata = positiveSampler.sample(data, 50);\n\nSystem.out.println(data.head(10));\n\nSingleLinkageClustering algorithm = new SingleLinkageClustering();\nalgorithm.setClusterCount(2);\n\nDataFrame learnedData = algorithm.fitAndTransform(data);\n\nfor(int i = 0; i \u003c learnedData.rowCount(); ++i){\n DataRow tuple = learnedData.row(i);\n String clusterId = tuple.getCategoricalTargetCell(\"cluster\");\n System.out.println(\"learned: \" + clusterId +\"\\tknown: \"+tuple.target());\n}\n```\n\n### Spatial Segmentation using DBSCAN\n\nThe following sample code shows how to use DBSCAN to perform clustering:\n\n```java\nDataQuery.DataFrameQueryBuilder schema = DataQuery.blank()\n      .newInput(\"c1\")\n      .newInput(\"c2\")\n      .newOutput(\"designed\")\n      .end();\n\nSampler.DataSampleBuilder negativeSampler = new Sampler()\n      .forColumn(\"c1\").generate((name, index) -\u003e randn() * 0.3 + (index % 2 == 0 ? 2 : 4))\n      .forColumn(\"c2\").generate((name, index) -\u003e randn() * 0.3 + (index % 2 == 0 ? 2 : 4))\n      .forColumn(\"designed\").generate((name, index) -\u003e 0.0)\n      .end();\n\nSampler.DataSampleBuilder positiveSampler = new Sampler()\n      .forColumn(\"c1\").generate((name, index) -\u003e rand(-4, -2))\n      .forColumn(\"c2\").generate((name, index) -\u003e rand(-2, -4))\n      .forColumn(\"designed\").generate((name, index) -\u003e 1.0)\n      .end();\n\nDataFrame data = schema.build();\n\ndata = negativeSampler.sample(data, 200);\ndata = positiveSampler.sample(data, 200);\n\nSystem.out.println(data.head(10));\n\nDBSCAN algorithm = new DBSCAN();\nalgorithm.setEpsilon(0.5);\n\nDataFrame learnedData = algorithm.fitAndTransform(data);\n\nfor(int i = 0; i \u003c learnedData.rowCount(); ++i){\n DataRow tuple = learnedData.row(i);\n String clusterId = tuple.getCategoricalTargetCell(\"cluster\");\n System.out.println(\"learned: \" + clusterId +\"\\tknown: \"+tuple.target());\n}\n\n```\n\n### Image Segmentation (Clustering) using KMeans\n\nThe following sample code shows how to use FuzzyART to perform image segmentation:\n\n```java\nBufferedImage img= ImageIO.read(FileUtils.getResource(\"1.jpg\"));\n\nDataFrame dataFrame = ImageDataFrameFactory.dataFrame(img);\n\nKMeans cluster = new KMeans();\nDataFrame learnedData = cluster.fitAndTransform(dataFrame);\n\nfor(int i=0; i \u003clearnedData.rowCount(); ++i) {\n ImageDataRow row = (ImageDataRow)learnedData.row(i);\n int x = row.getPixelX();\n int y = row.getPixelY();\n String clusterId = row.getCategoricalTargetCell(\"cluster\");\n System.out.println(\"cluster id for pixel (\" + x + \",\" + y + \") is \" + clusterId);\n}\n```\n\nThe segmented image can be generated using the trained KMeans from above as illustrated by the following sample code:\n\n```java\n\nList\u003cInteger\u003e classColors = new ArrayList\u003cInteger\u003e();\nfor(int i=0; i \u003c 5; ++i){\n for(int j=0; j \u003c 5; ++j){\n    classColors.add(ImageDataFrameFactory.get_rgb(255, rand.nextInt(255), rand.nextInt(255), rand.nextInt(255)));\n }\n}\n\nBufferedImage segmented_image = new BufferedImage(img.getWidth(), img.getHeight(), img.getType());\nfor(int x=0; x \u003c img.getWidth(); x++)\n{\n for(int y=0; y \u003c img.getHeight(); y++)\n {\n    int rgb = img.getRGB(x, y);\n\n    DataRow tuple = ImageDataFrameFactory.getPixelTuple(x, y, rgb);\n\n    int clusterIndex = cluster.transform(tuple);\n\n    rgb = classColors.get(clusterIndex % classColors.size());\n\n    segmented_image.setRGB(x, y, rgb);\n }\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchen0040%2Fjava-clustering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchen0040%2Fjava-clustering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchen0040%2Fjava-clustering/lists"}