1. Integration of association rules into WUM Bastian Germershaus

2. Seminar Webmining, Institut für Wirtschaftsinformatik 2 contents introduction WUM web usage miner in general association rules – a brief example association rules – the theory association rules in WUM association rules in WUM – a demo

3. Seminar Webmining, Institut für Wirtschaftsinformatik 3 the problem we have a large amount of data (e.g. from the server log of our website) we would like to know if there are any rules in the behavior of our costumers we could use these rules later on to optimize our business

4. Seminar Webmining, Institut für Wirtschaftsinformatik 4 an example ( Amazon.COM, as usual )

5. Seminar Webmining, Institut für Wirtschaftsinformatik 5 looking at their sales data retrieving all orders by customer and current item building rules: if a customer bought book X → he also bought books X and/or Y and/or Z what Amazon.COM does

6. Seminar Webmining, Institut für Wirtschaftsinformatik 6 contents introduction WUM web usage miner in general association rules – a brief example association rules – the theory association rules in WUM association rules in WUM – a demo

7. Seminar Webmining, Institut für Wirtschaftsinformatik 7 WUM - web usage miner (1) main goal: navigation pattern discovery  sequence of pages through the website  typical patterns  optimization of site navigation three steps  log file cleaning  pattern analysis  visualization

8. Seminar Webmining, Institut für Wirtschaftsinformatik 8 WUM – web usage miner (2) Source: Myra Spiliopoulou: “Web Usage Mining for Web Site Evaluation” in Communications of the ACM, August 2000, Vol. 43

9. Seminar Webmining, Institut für Wirtschaftsinformatik 9 WUM – web usage miner (3) special requirements  miner should understand abstract pattern descriptions  ‘MINT’ (SQL-like query language)  usage patterns should be more than a sequence of frequently accessed pages integration of statistics about the routes connecting pages frequently accessed together

10. Seminar Webmining, Institut für Wirtschaftsinformatik 10 WUM – web usage miner (4) Source: Myra Spiliopoulou: “Web Usage Mining for Web Site Evaluation” in Communications of the ACM, August 2000, Vol. 43

11. Seminar Webmining, Institut für Wirtschaftsinformatik 11 WUM – web usage miner (5) evaluation of discovered patterns is needed  statistical testing  semantic evaluation discovered navigation patterns may help restructuring the site  redesign pages, inserting links  restructuring may confuse some users

12. Seminar Webmining, Institut für Wirtschaftsinformatik 12 contents introduction WUM web usage miner in general association rules – a brief example association rules – the theory association rules in WUM association rules in WUM – a demo

13. Seminar Webmining, Institut für Wirtschaftsinformatik 13 association rules (1) example  we sell cell-phones, gadgets and accessories Homepage (H) cell-phones (C1) Nokia (C21) 3110 (C211) 8110 (C212) Siemens (C22) C35 (C221) S 45 (C222) gadgets (G1) Palm (G11) Palm III (G111) Palm V (G112) Compaq (G12) Ipaq (G121) accessories (A1) Nokia (A11) hands-free kit (A111) Siemens (A12) hands-free kit (A121)

14. Seminar Webmining, Institut für Wirtschaftsinformatik 14 association rules (2) possible association rule C212 (13)G121 & A111 (30) Support: 0,065 (6,5 %)Confidence: 0,433 (43,3 %) 200 different orders in database 13 of 30 users, that bought a Compaq Ipaq and a hands- free kit for Nokia phones also bought a Nokia 8110.

15. Seminar Webmining, Institut für Wirtschaftsinformatik 15 association rules (3) sequence of pages does NOT matter ‘if – then – condition’ parameters (support, confidence) useful rules  apply reasonably often (  support)  are unusually reliable (  confidence)  make interesting predictions

16. Seminar Webmining, Institut für Wirtschaftsinformatik 16 contents introduction WUM web usage miner in general association rules – a brief example association rules – the theory association rules in WUM association rules in WUM – a demo

17. Seminar Webmining, Institut für Wirtschaftsinformatik 17 association rules in general (1) “if a customer came to our website through a banner and it is not his first visit then he buys an article” this object has three attributes:  came through banner  at least second visit  buys an article

18. Seminar Webmining, Institut für Wirtschaftsinformatik 18 association rules in general (2) binary attributes (0 or 1; yes or no) rules should have the form if attribute X ► then attribute Y (X → Y) attributes should be disjunctive (X ∩ Y=Ø)

19. Seminar Webmining, Institut für Wirtschaftsinformatik 19 association rules in general (3) parameters for association rules:  confidence “60% where attribute X is true → attribute Y is also true”  support “40% where attribute X is true → attribute Y is also true; that applies to 10% of all cases in the database”

20. Seminar Webmining, Institut für Wirtschaftsinformatik 20 association rules in general (4) the goal of the used Apriori algorithm is: “find all rules where minimum support and minimum confidence holds true” two iterative steps  find ‘large item sets’ with minimum support  candidate-generating and pruning

21. Seminar Webmining, Institut für Wirtschaftsinformatik 21 association rules in general (5) find large item sets  support-calculation for every candidate (support means occurrence of candidate in relation to whole number of objects)  remove every candidate with smaller support then ‘minimum support’  save candidates with high incidence

22. Seminar Webmining, Institut für Wirtschaftsinformatik 22 association rules in general (6) candidate-generating and pruning  temporary candidates: for two sets X, Y of cardinality n, which have n-1 attributes in common, build a temporary candidate X U Y  pruning: eliminate all candidates, where support of each candidate with a cardinality of n is lower than min. support

23. Seminar Webmining, Institut für Wirtschaftsinformatik 23 contents introduction WUM web usage miner in general association rules – a brief example association rules – the theory association rules in WUM association rules in WUM – a demo

24. Seminar Webmining, Institut für Wirtschaftsinformatik 24 association rules in WUM (1)

25. Seminar Webmining, Institut für Wirtschaftsinformatik 25 association rules in WUM (2)

26. Seminar Webmining, Institut für Wirtschaftsinformatik 26 contents introduction WUM web usage miner in general association rules – a brief example association rules – the theory association rules in WUM association rules in WUM – a demo

27. Seminar Webmining, Institut für Wirtschaftsinformatik 27 contents introduction WUM web usage miner in general association rules – a brief example association rules – the theory association rules in WUM association rules in WUM – a demo