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Amol Singh commented on MATH-1367: ---------------------------------- Hi Giles, Thanks for the quick response. Here's a simple breaking test, you should be able to add this to DBSCANClustererTest.java and run it. @Test public void testBreakingCase() { final DoublePoint[] points = { new DoublePoint(new double[] { 21.345289965479466, 32.149537670215295 }), new DoublePoint(new double[] { 42.02226837841131, 19.69611946377732 }), new DoublePoint(new double[] { 41.930019221367985, 21.954397109962457 }), new DoublePoint(new double[] { 42.87345060400816, 18.796775009724836 }) }; final DBSCANClusterer<DoublePoint> clusterer = new DBSCANClusterer<DoublePoint>(3, 3); List<Cluster<DoublePoint>> clusters = clusterer.cluster(Arrays.asList(points)); Assert.assertEquals(1, clusters.size()); final List<DoublePoint> clusterOne = Arrays.asList(points[1], points[2], points[3]); Assert.assertTrue(clusters.get(0).getPoints().containsAll(clusterOne)); } Now, if you set minPts=2 or change the code to remove the reference check of the point to itself in getNeighbors() this will pass. Let me know if you agree this is a bug. I'm happy to submit a patch. This change does change the behavior for existing users, the algorithm will produce more clusters / shape of clusters may change. Nonetheless I feel this would be the correct interpretation of the DBSCAN paper, and it seems consistent with other third party libraries I've evaluated. > DBSCAN Implementation does not count the seed point itself as part of its > neighbors count > ----------------------------------------------------------------------------------------- > > Key: MATH-1367 > URL: https://issues.apache.org/jira/browse/MATH-1367 > Project: Commons Math > Issue Type: Bug > Affects Versions: 3.6.1 > Reporter: Amol Singh > Fix For: 4.0 > > > The DSCAN paper describes the eps-neighborhood of a point as > https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf (Page 2) > Definition 1: (Eps-neighborhood of a point) The Eps-neighborhood of a point > p, denoted by NEps(p), is defined by NEps(p) = {q ∈ D | dist(p,q)< Eps} > in other words for all q points that are a member of database D whose > distance from p is less that Eps should be classified as a neighbor. This > should include the point itself. > The implementation however has a reference check to the point itself and does > not add it to its neighbors list. > private List<T> getNeighbors(final T point, final Collection<T> points) { > final List<T> neighbors = new ArrayList<T>(); > for (final T neighbor : points) { > if (point != neighbor && distance(neighbor, point) <= eps) { > neighbors.add(neighbor); > } > } > return neighbors; > } > "point != neighbor " check should be removed here. Keeping this check > effectively is raising the minPts count by 1. Other third party QuadTree > backed DBSCAN implementations consider the center point in its neighbor count > E.g. bmw-carit library. > If this is infact by design, the check should use value equality instead of > reference equality. T extends Clusterable<T> , the client should be able to > define this behavior. -- This message was sent by Atlassian JIRA (v6.3.4#6332)