1. Spatial Congeries Pattern Mining Presented by: Iris Zhang Supervisor: Dr. David Cheung 24 October 2003

2. Outline Introduction Motivation Related work Formal definition Algorithms Experiments Conclusion

3. Introduction KDD Discovery of interesting, implicit, and previously unknown knowledge from large databases [FPM91] Spatial data mining Extraction of implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases [KH95]

4. Feature of Spatial Data Mining Spatial autocorrelation Everything is related to everything else but nearby things are more related than distant things (Tobler, 1979) Spatial heterogeneity The variation in spatial data is a function of location

5. Motivation A famous historical example In 1909, the residents of Colorado Springs were discovered that they had healthy teeth and the local drinking water had high level of fluoride. Researchers confirmed the positive role of fluoride in controlling tooth decay. {healthy teeth, high level of fluoride}

6. Motivation (Cont’) Another case [HSX02]

7. Related work Neighboring Class Sets Mining Co-location Pattern Mining

8. Neighboring Class Sets Access records of mobile services IDPositionServices… xxx(14975,27020)Weather… xxx(16723,24301)Timetable… xxx(15521,26441)Ticket… xxx……… (14737,26752)Timetable…

9. Neighboring Class Sets Neighboring class sets ((timetable,ticket),4), ((timetable,weather)3), ((ticket,weather),2), ((timetable,ticket,weather),2) [Mor01]

10. Neighboring Class Sets Grouping of points [Mor01]

11. Neighboring Class Sets Grouping of points [Mor01]

12. Neighboring Class Sets Grouping of points [Mor01]

13. Neighboring Class Sets Apriori generation of valid instances [Mor01]

14. Problems Undercount the number of instances Depend on the order of classes to generate instances for k-neighboring class set (k>2) Provide an absolute number to be support threshold

15. Co-location Patterns Mining Co-location: a subset of Boolean features E.g.: (drought, EL Nino, substantial increase in vegetation, extremely high precipitation)

16. Co-location Patterns Mining Row instance I ={i 1,i 2,…,i k } of a co- location C={f 1,f 2,…,f k }: i j is an instance of f j (j = 1,2,…k) i p and i q are neighbors to each other (A.1,B.1) is a row instance of co-location {A,B} Table instance T of C is the set of all row instances of C {(A.1,B.1), (A.2,B.4), (A.3,B.4)} is table instance of {A,B}

17. Co-location Patterns Mining Participant ratio for feature f i : Pr({A,B},A}=3/4=75%, Pr({A,B},B}=2/5=40% Participant index of a co-location C: Pi({A,B})=min(0.75,0.4)=0.4

18. Co-location Pattern Mining Co-location rule: C 1  C 2 (p,cp) C 1 and C 2 are co-locations C 1  C 2 =  p: participant index, cp: conditional probability {A}  {B}(40%, 75%) Conditional probability of a co-location rule:

19. Co-location Patterns Mining Apriori-property Participant index is monotonically non- increasing as the size of the co-location increasing Apriori-like mining algorithm Candidate generation Instances generation

20. Co-location Patterns Mining Candidate generation Join Prune

21. Co-location Patterns Mining Instance generation Geometric approach Rtree join Combinatorial approach Sort-merge join Hybrid approach Rtree join to get instances for size 2 co-location Sort-merge join to get instances for size k(k>2) co-location

22. Co-location Patterns Mining Example

23. Problems The participant index measure may overate some co-location The features are binary Pr({A,B},A)=2/8=25% Pr({A,B},B)=6/6=100% Pi({A,B})=min(25%,100%)=25% {B}  {A}(25%, 100%) {A}  {B}(25%, 25%) Probability({A,B})=7/(8*6)  15%

24. Spatial Congeries Patterns Mining Input: D = {D 1,D 2,…,D n } Spatial relation to regulate the relation of objects in patterns min_fre threshold to determine whether an itemset is frequent Output: Complete set of Spatial Congeries patterns

25. Spatial Congeries Patterns Mining Example of datasets *Attribute values can be translated to categorical values ** {VD:10 WD:shallow DOP: near NL:existent} can be a pattern IDAttributeTypeDescription D1Vegetation durabilityOrdinalOrdinate scale from 10 to 100 D2Water depthNumericIn centimeters D3Distance to open waterNumericIn meters D4Nest locationBinaryExistence or absence of bird nest

26. Formal Definition Item: an attribute value in a dataset. I is the set of all items. E.g.: water depth: shallow Itemset: subset of I E.g.: VD:10 WD:shallow DOP: near N:existent E.g.: VD:100 WD:depth DOP:far N:absent

27. Formal Definition Spatial relation: rule to regulate the spatial relation of objects in patterns Instances of an item i: points which has attribute value i Instances of an itemset: if instances of all items in the itemset satisfy the spatial relation, the combination of these instances is an instance of the itemset.

28. Observation The number of instances of itemsets is not monotonically non-increasing E.g.: an instance of {triangle, circle} can construct two instances of {triangle, circle, rectangle} Conclusion: the number of instances of an itemset can be used to be the measure to determine whether the itemset is a pattern

29. Formal Definition Frequency of an itemset: Number of instances of the itemset over all possible combinations of instances of items E.g.: Frequency({A,B})=7/(8*6)  15%

30. Formal Definition Spatial Congeries pattern: If the frequency of an itemset is no less than frequency threshold min_fre, the itemset is a Spatial Congeries pattern.

31. Property of Frequency Lemma: the frequency of an itemset is monotonically non-increasing with the size of the itemset increasing. Proof: (simplified) For size k-1 itemset I k-1 ={v 1, v 2,…, v k-1 } and size k itemset I k = {v 1, v 2,…, v k-1, v k } *m q is the number of instances of I q **n q is the number of instances of item v q.

32. Algorithm-1 Step 1: generate complete set of size 2 patterns by Rtree-join on complete Rtrees

33. Algorithm-1 Step 1: generate complete set of size 2 patterns by Rtree-join on complete Rtrees

34. Algorithm-1 Step 1: generate complete set of size 2 patterns by Rtree-join on complete Rtrees

35. Algorithm-1 Step 1: generate complete set of size 2 patterns by Rtree-join on complete Rtrees

36. Algorithm-1 Step 2:generate size k (k>2) patterns level by level Generate size k (k>2) candidates Join two size k-1 patterns Prune those candidates which have subsets that are not frequent Generate size k (k>2) instances

37. Sample Square: a1 Triangle: a2 Circle: b1 Diamond: c1 a2Y5X5 a1Y4X4 a1Y3X3 a2Y2X2 a1Y1X1 b1Y8X8 b1Y7X7 b1Y6X6 c1Y9X9 Datasets A Datasets B Datasets C

38. Process of Algorithm-1 RJ to find the instances of size 2 candidates Build Rtree for each dataset A, B and C Do RJ find the instances of size 2 candidates m a1b1 = 5, m a2b1 =3, m a1c1 = 2, m a2c1 = 0, m b1c1 = 0 Get size 2 patterns a1b1, a2b1,a1c1 according to the frequency threshold 50% f a1b1 = 5/(3*3)  56%, f a2b1 = 3/(2*3) = 50%, f a1c1 = 2/(3*1)  67%, f a2c1 = 0 f b1c1 = 0

39. Process of Algorithm-1 Sort-merge-join to find the instances of size k (k>2) candidates Generate size 3 candidates Join size 2 pattern a1b1 and a1c1 to form a1b1c1 Prune a1b1c1 because b1c1 is not a pattern Get size 3 patterns ( there is no size 3 patterns)

40. Algorithm-2 Step 1:generate all patterns for a combination of subsets. Each subset corresponds to an item. All points in the subset have the item as their attribute value. E.g.: The first combination is a1b1c1. It needs to build rtrees for subsets of a1, b1, c1 in order to generate size 2 patterns. Then it do sort-merge join to generate size k(k>2) patterns. Step 2: generate all patterns for another combination until there is no combination E.g.: The second combination is a2b1c1.

41. Process of Algorithm-2 Generate patterns for combination a1b1c1 RJ on Rtrees for a1, b1 and c1 to get instances of candidates a1b1, a1c1, b1c1 Suppose a1b1 and a1c1 are patterns, size 3 candidates is a1b1c1 Sort-merge-join to get instances of a1b1c1 Generate patterns for combination a2b1c1 RJ on Rtrees for a2, b1, c1 to get instances of candidates a2b1 and a2c1. Because the instances of b1c1 has been generated, there is no need to do it again Suppose a2b1 is pattern There is no size 3 candidate

42. Experiment Environment CPU type: Pentium III Xeon 700MHz RAM: 4096M Dataset Synthetic dataset with Gauss distribution No. of clusters: 5 Map size: 800 E.g.: (622, 478, 5) is a point in a dataset

43. Experiment-1 *No. of Datasets: 3 *No. of Attribute Values: 5 *Distance threshold : 100 *Frequency threshold: 0.01

44. Experiment-1 *No. of Datasets: 3 *No. of Attribute Values: 5 *Distance threshold : 100 *Frequency threshold: 0.01

45. Experiment-1 *No. of Datasets: 3 *No. of Attribute Values: 5 *Distance threshold : 100 *Frequency threshold: 0.01

46. Experiment-2 *No. of Points in each datasets: 1000 *No. of Attribute Values: 5 *Distance threshold : 100 *Frequency threshold: 0.01

47. Experiment-3 *No. of Datasets: 5 *No. of Points in each datasets: 1000 *No. of Attribute Values: 5 *Distance threshold: 100

48. Experiment-4 *No. of Datasets: 3 *No. of Points in each datasets: 1000 *No. of Attribute Values: 5 *Frequency threshold: 0.01

49. Conclusions Neighboring class set mining and co-location pattern mining problem are introduced Spatial Congeries pattern mining is formulated and provided with two Apriori-like mining algorithms Future work: More pruning methods should be used to reduce the time and space requirement The experiments should be done on real datasets

50. References [HSX02] Huang Y., Shekhar S., Xiong H. Discovering Co-location Patterns from Spatial Datasets: A General Approach. Submited to IEEE TKED (under second round review) [HXSP03] Huang Y., Xiong H., Shekar S., Pei J. Mining Confident Co-location Rules without A Support Threshold. Proc. of 18 th ACM Symposium on Applied Computing (ACM SAC), 2003 [Mor01] Morimoto Y. Mining Frequent Neighboring Class Sets in Spatial Databases. Proc. of ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2001.

51. Q&A