Elki GDBSCAN Java/Scala -如何修改CorePredicate

Question

elki中的通用dbscan (gdbscan)是如何在Java/Scala中实现的？我目前正在努力寻找一种在elki上实现加权dbscan的有效方法，以弥补加权dbscan的sklearn实现带来的低效。

我现在这么做的原因是因为sklearn在at级的数据集(在云上，在本例中就是我)上的集群上实现dbscan太糟糕了。

例如，我使用数据库创建函数和dbscan函数编写了以下代码，该函数读取数组的数组，并输出集群索引的索引。

/* Libraries imported from the ELKI library - https://elki-project.github.io/releases/current/doc/overview-summary.html */
import de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansElkan 
import de.lmu.ifi.dbs.elki.data.model.{ClusterModel, DimensionModel, KMeansModel, Model} 
import de.lmu.ifi.dbs.elki.data.model
import de.lmu.ifi.dbs.elki.data.{Clustering, DoubleVector, 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.distance.distancefunction.minkowski.EuclideanDistanceFunction
import de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunction
import de.lmu.ifi.dbs.elki.algorithm.clustering.DBSCAN

// Imports for generalized DBSCAN
import de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan // Generalized dbscan function here required for weighted dbscan
import de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.CorePredicate // THIS IS IMPORTANT TO GET GENERALIZED DBSCAN
import de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.GeneralizedDBSCAN
import de.lmu.ifi.dbs.elki.utilities.ELKIBuilder

import de.lmu.ifi.dbs.elki.database.relation.Relation
import de.lmu.ifi.dbs.elki.datasource.DatabaseConnection
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter
import de.lmu.ifi.dbs.elki.index.tree.metrical.covertree.SimplifiedCoverTree
import de.lmu.ifi.dbs.elki.data.{`type`=>TYPE} // Need to import in this way as 'type' is a class method in Scala
import de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTreeFactory // Important

def createDatabaseWeighted(data: Array[Array[Double]], distanceFunction: NumberVectorDistanceFunction[NumberVector]): Database = {
  val indexFactory = new SimplifiedCoverTree.Factory[NumberVector](distanceFunction, 0, 30)
  // Create a database
  val db = new StaticArrayDatabase(new ArrayAdapterDatabaseConnection(data), java.util.Arrays.asList(indexFactory))
  // Load the data into the database
  val CustomPredicate = CorePredicate
  db
}

def dbscanClusteringOriginalTest(data: Array[Array[Double]], distanceFunction: NumberVectorDistanceFunction[NumberVector] = SquaredEuclideanDistanceFunction.STATIC, epsilon: Double = 10, minpts: Int = 10) = {
  // Use the same `distanceFunction` for the database and DBSCAN <- is it required??
  val db = createDatabaseWeighted(data, distanceFunction)
  val rel = db.getRelation(TYPE.TypeUtil.NUMBER_VECTOR_FIELD) // Create the required relational database
  val dbscan = new DBSCAN[DoubleVector](distanceFunction, epsilon, minpts) // Epsilon and minpoints needed - either you define in the function input, or will use default values
  val result: Clustering[Model] = dbscan.run(db)
  var ClusterCounter = 0 // Indexing the number of datapoints allocated from DBSCAN

  result.getAllClusters.asScala.zipWithIndex.foreach { case (cluster, idx) =>
    println("The type is " + cluster.getNameAutomatic)
    /* Isolate only the clusters and store the median from the DBSCAN results */
    if (cluster.getNameAutomatic == "Cluster" || cluster.getNameAutomatic == "Noise") {
      ClusterCounter += 1
      val ArrayMedian =  Array[Double]()
      println(s"# $idx: ${cluster.getNameAutomatic}")
      println(s"Size: ${cluster.size()}")
      println(s"Model: ${cluster.getModel}")
      println(s"ids: ${cluster.getIDs.iter().toString}")
    }
  }
}

我可以让它非常高效地运行，但我目前正在努力研究如何使用gdbscan函数获得类似的效果。例如，有一个答案建议这可以通过修改ELKI上的CorePredicate (sample_weight option in the ELKI implementation of DBSCAN)来完成，但我不确定如何实现这一点。

如有任何建议，我们将不胜感激！

Answer 1

实现您自己的GDBSCAN核心谓词。

而不是像标准实现中那样计算邻居，而是添加它们的权重。

然后，对DBSCAN进行加权。