1. A Data Mining Approach for Location Prediction in Mobile Environments Data & Knowledge Engineering Volume 54, Issue 2, August 2005, Pages 121–146 劉康全 1

2. Introduction 2

3. Motivation Predicted movement can be used for effectively allocating resources instead of blindly allocating excessive resources Benefit to the broadcast program generation, data items can be broadcast to the predicted cell 3

4. Definition UAP IDUAP 1 2 3 4 Database of UAPs An example coverage region and corresponding graph G 4

5. Definition example: UAP A= and pattern B= A = 3 4 0 1 5 B = 4 - - 5 5

6. Definition example: R: , = 2, = 1.33 6

7. Three phases for the algorithm 1.Mining UMPs from Graph Traversals: Find mobility patterns 2.Generation of Mobility Rules: Find Mobility rules from mobility patterns 3.Mobility Prediction: Prediction of next inter-cell movement based on mobility rules 7

8. UMP mining UAP IDUAP 1 2 3 4 Database of UAPs corresponding graph G minsup=1.33 8

9. UMP mining UAP IDUAP 1 2 3 4 Database of UAPs corresponding graph G minsup=1.33 9

10. UMP mining UAP IDUAP 1 2 3 4 Database of UAPs corresponding graph G minsup=1.33 10

11. UMP mining UAP IDUAP 1 2 3 4 11

12. UMP Mining 12

13. UMP mining 13

14. UMP Mining 14

15. CandidateGeneration 15

16. Three phases for the algorithm 1.Mining UMPs from Graph Traversals: Movement data mined for discovering regularities (UMP) in inter-cell movements? 2.Generation of Mobility Rules: Mobility rules are extracted from UMPs? 3.Mobility Prediction: Prediction of next inter-cell movement based on mobility rules 16

17. Mobility Rules minconf=50% 17

18. Three phases for the algorithm 1.Mining UMPs from Graph Traversals: Movement data mined for discovering regularities (UMP) in inter-cell movements? 2.Generation of Mobility Rules: Mobility rules are extracted from UMPs? 3.Mobility Prediction: Prediction of next inter-cell movement based on mobility rules 18

19. Mobility Prediction Example: Assume that the current trajectory of the user is P= Matching Rules: (support+confidence) → 1.5+50=51.5 → 2.5+83.33=85.83 → 1.5+75=76.5 Sorted tuple array is: TupleArray = [(5, 85.83), (0, 76.5)] If m=1, then Predicted Cells Set = {5} If m=2, then Predicted Cells Set = {5, 0} 19

20. Mobility Prediction 20

21. Precision & Recall 女男 判斷女 TP=15FP=35 判斷男 FN=5TN=45 假如已知有 80 位男生， 20 位女生，總共 100 人。目標是找出所有女生。 某人挑出 50 人，其中 15 人是女生另外把 35 人是男生被當成女生挑出來。 Precision = 15 / ( 15 + 35 ) = 0.3 Recall = 15 / ( 15 + 5 ) = 0.75 21

22. 22 Impact of m on Precision

23. 23 Impact of m on Recall

24. 24 Impact of Supp min

25. 25 Impact of Supp min

26. 26 Impact of Conf min

27. 27

28. Conclusion A data mining algorithm for the prediction of user movements in a mobile computing system Algorithm is based on – Mining the mobility patterns of users – Then forming mobility rules from these patterns – Finally predicting a mobile user’s next movements by using the mobility rules A good performance when compared to the performance of Ignorant Method 28