Efficient Algorithms for Non-Parametric Clustering With Clutter

1. Efficient Algorithms for Non-Parametric Clustering With Clutter Weng-Keen Wong Andrew Moore

2. Problems From the Physical Sciences Minefield detection (Dasgupta and Raftery 1998) Earthquake faults (Byers and Raftery 1998)

3. Problems From the Physical Sciences (Pereira 2002) (Sloan Digital Sky Survey 2000)

4. A Simplified Example

5. Clustering with Traditional Algorithms Single Linkage Clustering Mixture of Gaussians with a Uniform Background Component

6. Clustering with CFF Original Dataset Cuevas-Febrero-Fraiman

7. Related Work (Dasgupta and Raftery 98) • Mixture model approach – mixture of Gaussians for features, Poisson process for clutter (Byers and Raftery 98) • K-nearest neighbour distances for all points modeled as a mixture of two gamma distributions, one for clutter and one for the features • Classify each data point based on which component it was most likely generated from

8. Outline 1. Introduction: Clustering and Clutter 2. The Cuevas-Febreiro-Fraiman Algorithm 3. Optimizing Step One of CFF 4. Optimizing Step Two of CFF 5. Results

9. The CFF Algorithm Step One Find the high density datapoints

10. The CFF Algorithm Step Two • Cluster the high density points using Single Linkage Clustering • Stop when link length > 

11. The CFF Algorithm • Originally intended to estimate the number of clusters • Can also be used to find clusters against a noisy background

12. Step One: Non-Parametric Density Estimator A datapoint is a high density datapoint if: The number of datapoints within a hypersphere of radius h is > threshold c

13. Speeding up the Non-Parametric Density Estimator • Addressed in a separate paper (Gray and Moore 2001) • Two basic ideas: 1. Use a dual tree algorithm (Gray and Moore 2000) 2. Cut search off early without computing exact densities (Moore 2000)

14. Step Two: Euclidean Minimum Spanning Trees (EMSTs) • Traditional MST algorithms assume you are given all the distances • Implies O(N2) memory usage • Want to use a Euclidean Minimum Spanning Tree algorithm

15. Optimizing Clustering Step • Exploit recent results in computational geometry for efficient EMSTs • Involves modification to GeoMST2 algorithm by (Narasimhan et al 2000) • GeoMST2 is based on Well-Separated Pairwise Decompositions (WSPDs) (Callahan 1995) • Our optimizations gain an order of magnitude speedup, especially in higher dimensions

16. Outline for Optimizing Step Two 1. High level overview of GeoMST2 2. Example of a WSPD 3. More detailed description of GeoMST2 4. Our optimizations

17. Intuition behind GeoMST2

18. Intuition behind GeoMST2

19. High Level Overview of GeoMST2 1. Create the Well-Separated Pairwise Decomposition (A1,B1) (A2,B2) . . . (Am,Bm)

20. High Level Overview of GeoMST2 1. Create the Well-Separated Pairwise Decomposition Each Pair (Ai,Bi) represents a possible edge in the MST (A1,B1) (A2,B2) . . . (Am,Bm)

21. High Level Overview of GeoMST2 1. Create the Well-Separated Pairwise Decomposition (A1,B1) (A2,B2) . . . (Am,Bm) 2. Take the pair (Ai,Bi) that corresponds to the shortest edge 3. If the vertices of that edge are not in the same connected component, add the edge to the MST. Repeat Step 2.

22. A Well-Separated Pair (Callahan 1995) • Let A and B be point sets in d • Let RA and RB be their respective bounding hyper-rectangles • Define MargDistance(A,B) to be the minimum distance between RA and RB

23. A Well-Separated Pair (Cont) The point sets A and B are considered to be well-separated if: MargDistance(A,B)  max{Diam(RA),Diam(RB)}

24. A Well-Separated Pairwise Decomposition Pair #1: ([0],[1]) Pair #2: ([0,1], [2]) Pair #3: ([0,1,2],[3,4]) Pair #4: ([3], [4]) The set of pairs {([0],[1]), ([0,1], [2]), ([0,1,2],[3,4]), ([3], [4])} form a Well-Separated Pairwise Decomposition.

25. The Size of a WSPD A WSPD (A1,B1) (A2,B2) . . . (Am,Bm) If there are n points, a WSPD can be constructed with O(n) pairs using a fair split tree (Callahan 1995)

26. High Level Overview of GeoMST2 1. Create the Well-Separated Pairwise Decomposition (A1,B1) (A2,B2) . . . (Am,Bm) 2. Take the pair (Ai,Bi) that corresponds to the shortest edge 3. If the vertices of that edge are not in the same connected component, add the edge to the MST. Repeat Step 2

27. Bichromatic Closest Pair Distance Given two sets (Ai,Bi), the Bichromatic Closest Pair Distance is the closest distance from a point in Ai to a point in Bi

28. High Level Overview of GeoMST2 1. Create the Well-Separated Pairwise Decomposition (A1,B1) (A2,B2) . . . (Am,Bm) 2. Take the pair (Ai,Bi) with the shortest BCP distance 3. If Ai and Bi are not already connected, add the edge to the MST. Repeat Step 2.

29. GeoMST2 Example Start Current MST

30. GeoMST2 Example Iteration 1 Current MST

31. GeoMST2 Example Iteration 2 Current MST

32. GeoMST2 Example Iteration 3 Current MST

33. GeoMST2 Example Iteration 4 Current MST

34. High Level Overview of GeoMST2 1. Create the Well-Separated Pairwise Decomposition Modification for CFF: If BCP distance > , terminate (A1,B1) (A2,B2) . . . (Am,Bm) 2. Take the pair (Ai,Bi) with the shortest BCP distance 3. If Ai and Bi are not already connected, add the edge to the MST. Repeat Step 2.

35. Optimizations • We don’t need the EMST • We just need to cluster all points that are within  distance or less from each other • Allows two optimizations to GeoMST2 code

36. High Level Overview of GeoMST2 Optimizations take place in Step 1 1. Create the Well-Separated Pairwise Decomposition (A1,B1) (A2,B2) . . . (Am,Bm) 2. Take the pair (Ai,Bi) with the shortest BCP distance 3. If Ai and Bi are not already connected, add the edge to the MST. Repeat Step 2.

37. Optimization 1 Illustration

38. Optimization 1 Ignore all links that are >  • Every pair (Ai, Bi) in the WSPD becomes an edge unless it joins two already connected components • If MargDistance(Ai,Bi) > , then an edge of length  cannot exist between a point in Ai and Bi • Don’t include such a pair in the WSPD

39. Optimization 2 Illustration

40. Optimization 2 • Join all elements that are within  distance of each other • If the max distance separating the bounding hyper-rectangles of Ai and Bi is  , then join all the points in Ai and Bi if they are not already connected • Do not add such a pair (Ai,Bi) to the WSPD

41. Implications of the optimizations • Reduce the amount of time spent in creating the WSPD • Reduce the number of WSPDs, thereby speeding up the GeoMST2 algorithm by reducing the size of the priority queue

42. Results • Ran step two algorithms on subsets of the Sloan Digital Sky Survey • Compared Kruskal, GeoMST2, and -clustering • 7 attributes – 4 colors, 2 sky coordinates, 1 redshift value

43. Results (GeoMST2 vs -Clustering vs Kruskal in 4D)

44. Results (GeoMST2 vs -Clustering in 3D)

45. Results (GeoMST2 vs -Clustering in 4D)

46. Results (Change in Time as  changes for 4D data)

47. Results (Increasing Dimensions vs Time

48. Conclusions • -clustering outperforms GeoMST2 by nearly an order of magnitude in higher dimensions • Combining the optimizations in both steps will yield an efficient algorithm for clustering against clutter on massive data sets