DBSCAN及其索引是否要求具有相同的距離函數？如果不是，什么情況下需要使用不同的距離函數？Scala 代碼如何創建 DBSCAN 和索引：import de.lmu.ifi.dbs.elki.algorithm.clustering.DBSCANimport de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel.ParallelGeneralizedDBSCANimport de.lmu.ifi.dbs.elki.data.model.Modelimport 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.ArrayAdapterDatabaseConnectionimport de.lmu.ifi.dbs.elki.distance.distancefunction.NumberVectorDistanceFunctionimport de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunctionimport de.lmu.ifi.dbs.elki.index.tree.metrical.covertree.SimplifiedCoverTreedef createDatabase(data: Array[Array[Double]], distanceFunction: NumberVectorDistanceFunction[NumberVector]): Database = {  val indexFactory = new SimplifiedCoverTree.Factory[NumberVector](distanceFunction, 1.3, 20)  // Create a database  val db = new StaticArrayDatabase(new ArrayAdapterDatabaseConnection(data), java.util.Arrays.asList(indexFactory))  // Load the data into the database  db.initialize()  db}def dbscanClustering(data: Array[Array[Double]], distanceFunction: NumberVectorDistanceFunction[NumberVector]): Unit = {  // Use the same `distanceFunction` for the database and DBSCAN <- is it required??  val db = createDatabase(data, distanceFunction)  val dbscan = new DBSCAN[DoubleVector](distanceFunction, 0.01, 20)  val result: Clustering[Model] = dbscan.run(db)  println(s"Number of clusters: ${result.getAllClusters.size()}")  result.getAllClusters.asScala.zipWithIndex.foreach { case (cluster, idx) =>    println(s"# $idx: ${cluster.getNameAutomatic}")    println(s"Size: ${cluster.size()}")    println(s"Model: ${cluster.getModel}")}val inputData: Array[Array[Double]] = ???dbscanClustering(inputData, SquaredEuclideanDistanceFunction)