1 / 17

Discovering Frequent Arrangements of Temporal Intervals

Discovering Frequent Arrangements of Temporal Intervals. Papapetrou , P . ; Kollios, G. ; Sclaroff, S. ; Gunopulos , D . ICDM 2005. Outlines. Introduction D efinition The Arrangement Enumeration Tree BFS-based Approach DFS-based Approach Hybrid DFS-based Approach

jerry
Download Presentation

Discovering Frequent Arrangements of Temporal Intervals

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Discovering Frequent Arrangements of Temporal Intervals Papapetrou, P. ; Kollios, G. ; Sclaroff, S. ; Gunopulos, D. ICDM 2005

  2. Outlines • Introduction • Definition • The Arrangement Enumeration Tree • BFS-based Approach • DFS-based Approach • Hybrid DFS-based Approach • Experimental Evaluation • Conclusion

  3. Introduction • In this paper the goal is to develop methods that discover temporal arrangements of correlated event intervals which occur frequently in a database. • BFS-based Approach • DFS-based Approach • Hybrid DFS-based Approach

  4. Definition • Event Interval Temporal Relations

  5. Cont. • event interval sequence or e-sequence : • . • k-e-sequence • E.g. • 5-e-sequence • {(A,1,7), (B,3,19), (D,4,30), (C,7,15), (C,23,42)} • an e-sequence database

  6. Cont. • E.g. • This can be done by using the “AND” operand denoted by * . • (b) A|B * A|C * B>C • R = { | , || , > , →} and * . A|B→C A|B>C A|B>C

  7. The Arrangement Enumeration Tree N(1) N(2) N(k)

  8. BFS-based Approach • The BFS-based approach uses an arrangement enumeration tree to discover the set of frequent arrangements. • Definition : • Fk denote the complete set of frequent k-arrangements. • Ck the set of candidate frequent k-arrangements.

  9. Scanning D and filtering withmin_sup= 2 . • / • F1= {{A}, {B}, {C}}

  10. Based on F1 and the enumeration tree, N2 is generated. • / N2

  11. For every pair of events in the arrangements, D is scanned to get all the types of relations between them, i.e. IM2. • / IM2

  12. If these relations satisfy the support threshold they are added to F2. • F2 :

  13. F3 : • / • Output : F = A > B * A > C * B > C

  14. DFS-based Approach • We must completely explore all the sub-arrangements on a path before moving to another one. • One more step is added to BFS-based Algorithm : • When a node is found to contain a frequent arrangement, each sub-arrangement is added to F and the corresponding expansions are made on the tree. • We can skip those expansions in the enumeration tree reducing the cost of computation.

  15. Hybrid DFS-based Approach • We use a hybrid DFS approach that generates the first two levels of the tree using BFS and then follows DFS for the rest of the tree. • This would compensate for the multiple database scans.

  16. Experimental Evaluation medium density sparse sparse medium density dense dense

  17. Conclusion • The BFS-based approach uses an arrangement enumeration tree to discover the set of frequent arrangements. • The DFS-based method further improves performance over BFS by reaching longer arrangements faster and hence eliminating the need for examining smaller subsets of these arrangements.

More Related