KMeans 在 ELKI 中的使用，综合示例

Posted 2023-03-12

【中文标题】KMeans 在 ELKI 中的使用，综合示例

【英文标题】：KMeans usage in ELKI, comprehensive example

【发布时间】：2019-11-16 05:37:37

【问题描述】：

我想使用 KMeans 聚类算法，例如 Scala 中的 ELKI 中的 Elkan's kmeans 来获取聚类的质心。一个全面的例子会很好，因为浏览 ELKI 文档有点困难。

【参考方案1】：

假设您的内存中已有可用数据。您可以使用Pure Java API 创建Database 并将此数据库用作聚类算法的输入。完整的 Scala 代码：

import scala.collection.JavaConverters._

import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansElkan
import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomUniformGeneratedInitialMeans
import de.lmu.ifi.dbs.elki.data.model.KMeansModel
import de.lmu.ifi.dbs.elki.data.Clustering, NumberVector
import de.lmu.ifi.dbs.elki.database.Database, StaticArrayDatabase
import de.lmu.ifi.dbs.elki.datasource.ArrayAdapterDatabaseConnection
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction
import de.lmu.ifi.dbs.elki.utilities.random.RandomFactory


def createDatabase(coords: Seq[(Double, Double)]): Database = 
  // Allocate 2-d array (matrix).
  val data = Array.ofDim[Double](coords.length, 2)
  // Fill the matrix
  coords.zipWithIndex.foreach 
    case ((x, y), idx) =>
      data.update(idx, Array(x, y))
  
  // Create a database
  val db = new StaticArrayDatabase(new ArrayAdapterDatabaseConnection(data), null)
  // Load the data into the database
  db.initialize()
  db


val nClusters = numberOfClustersForDemand // Set your number of clusters
val nIters = 1000
// Convert my own type to the seq of (Double, Double)
val coords = activities.map(act => (act.getCoord.getX, act.getCoord.getY))
val db: Database = createDatabase(coords)
// Create an instance of KMeansElkan clustering Algorithm
val kmeans: KMeansElkan[NumberVector] = new KMeansElkan[NumberVector](SquaredEuclideanDistanceFunction.STATIC, nClusters, nIters,
  new RandomUniformGeneratedInitialMeans(RandomFactory.DEFAULT),true)
// Run the algorithm
val result: Clustering[KMeansModel] = kmeans.run(db)
// Show the results
val clustersInfo: Array[ClusterInfo] = result.getAllClusters.asScala.zipWithIndex.map  case (cluster, idx) =>
  println(s"# $idx: $cluster.getNameAutomatic")
  println(s"Size: $cluster.size()")
  println(s"Model: $cluster.getModel")
  println(s"Center: $cluster.getModel.getMean.toVector")
  println(s"getPrototype: $cluster.getModel.getPrototype.toString")
  ClusterInfo(cluster.size, new Coord(cluster.getModel.getMean))
.toArray