问题
How is the generalised dbscan (gdbscan) in elki implemented in Java/Scala? I am currently trying to find an efficient way to implement a weighted dbscan on elki to offset the inefficiencies coming from the sklearn implementation of the weighted dbscan.
The reason I am doing this at the moment is because the sklearn simply sucks for implementing the dbscan on clusters on datasets on the terabyte scale (on the cloud, which in this case I am).
For example, I have made the following code with the database creation function and the dbscan function that reads an array of arrays, and spits out the indices of the cluster indices.
/* 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}")
}
}
}
I can get this to run quite efficiently, but I am currently struggling on how I can get a similar effect with the gdbscan function. For example, there was an answer that suggested that this could be done by modifying the CorePredicate on ELKI (sample_weight option in the ELKI implementation of DBSCAN) but I am not sure how this could be implemented.
Any pointers would be highly appreciated!
回答1:
Implement your own GDBSCAN core predicate.
Rather than counting neighbors as in the standard implementation, add their weights.
Then you have weighted DBSCAN.
来源:https://stackoverflow.com/questions/65077844/elki-gdbscan-java-scala-how-to-modify-the-corepredicate