机器学习 ELKI 使用心得 DBSCAN

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一下是我们使用elki 在自己的代码中调用DBSCAN 算法的一个例子int NumberofDatabase=2730*35;String file="E://RXM_DATA//weka_elki_data//HSV//Data_JD_20_1.arff";Pattern colSep = Pattern.compile("\\s*[,;\\s]\\s*");String quoteChars = "\"'";Pattern comment = Pattern.compile("^\\s*(#|//|;|@| ).*$");BitSet labelIndices = new BitSet();Parser parser=new NumberVectorLabelParser<>(colSep, quoteChars, comment, labelIndices, DoubleVector.FACTORY);DatabaseConnection dbc=new FileBasedDatabaseConnection(null, parser, file);Database db=new StaticArrayDatabase(dbc,null);db.initialize();EuclideanDistanceFunction distanceFunction=EuclideanDistanceFunction.STATIC;double  epsilon=0.1;int  minpts=50;int numberOfNeighbors=50;SharedNearestNeighborIndex.Factory<NumberVector, SharedNearestNeighborIndex<NumberVector>> indexFactory=new Factory(numberOfNeighbors, distanceFunction);SharedNearestNeighborSimilarityFunction<NumberVector> similarityFunction=new SharedNearestNeighborSimilarityFunction<NumberVector>(indexFactory);SNNClustering<NumberVector> snn=new SNNClustering(similarityFunction, epsilon, minpts);Clustering<Model> c=snn.run(db);Relation<NumberVector> relation = db.getRelation(TypeUtil.NUMBER_VECTOR_FIELD);DBIDRange ids = (DBIDRange) relation.getDBIDs();</span>int belong[] = new int[NumberofDatabase];int i = 0;for(Cluster<Model> clu : c.getAllClusters()) {System.out.println(clu.getNameAutomatic());  if(clu.getNameAutomatic().equals("Noise"))  {  for(DBIDIter it = clu.getIDs().iter(); it.valid(); it.advance()) {    final int offset = ids.getOffset(it);    belong[offset]=-1;    }    }  else  {  for(DBIDIter it = clu.getIDs().iter(); it.valid(); it.advance()) {    final int offset = ids.getOffset(it);    belong[offset]=i;    }  ++i;    } }FileWriter file1=new FileWriter("C://Users//Administrator//Desktop//Work//PPM_03//LM//landmark_0.09_50.dat");for(int i1=0;i1<NumberofDatabase;i1++){file1.write(belong[i1]+"\n");}file1.flush();file1.close();System.out.println("i="+i+"\n");
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