Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction   Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction   Mining for Blocks          Multiple-Level Multidimensional Sequential Patterns          Conclusion and pers...
Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction    Mining for Blocks    Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction   Mining for Blocks        Multiple-Level Multidimensional Sequential Patterns     Conclusion and perspective...
Introduction     Mining for Blocks       Multiple-Level Multidimensional Sequential Patterns    Conclusion and perspective...
Introduction     Mining for Blocks       Multiple-Level Multidimensional Sequential Patterns    Conclusion and perspective...
Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction   Mining for Blocks       Multiple-Level Multidimensional Sequential Patterns        Conclusion and perspecti...
Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction                          Mining for Blocks     Multiple-Level Multidimensional Sequential Patterns           ...
Introduction         Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives...
Introduction         Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives...
Introduction         Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives...
Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction       Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives

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Introduction      Mining for Blocks    Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives

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Introduction      Mining for Blocks    Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives

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Introduction      Mining for Blocks    Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives

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Introduction      Mining for Blocks    Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives

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Introduction      Mining for Blocks    Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives

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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks     Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives

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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction      Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives


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Introduction      Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives


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Introduction     Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives



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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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Introduction    Mining for Blocks   Multiple-Level Multidimensional Sequential Patterns   Conclusion and perspectives




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OLAP Mining: Mining Multidimensional Data

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OLAP Mining: Mining Multidimensional Data

  1. 1. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives OLAP Mining: Mining Multidimensional Data EXPEDO LIRMM, U NIVERSITÉ M ONTPELLIER II, F RANCE ETIS, U NIVERSITÉ C ERGY-P ONTOISE , F RANCE HELP UC, K UALA L UMPUR , M ALAYSIA Feb. 20-21 2007 EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  2. 2. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Outline 1 Introduction OLAP and Data Mining Research Topics on OLAP Mining (EXPEDO) 2 Mining for Blocks Fuzzy and Crisp Blocks Generating Blocks Managing Hierarchies Visualizing Blocks 3 Multiple-Level Multidimensional Sequential Patterns Multidimensional Sequential Patterns Multiple Level MSP Implementation 4 Conclusion and perspectives EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  3. 3. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Outline 1 Introduction OLAP and Data Mining Research Topics on OLAP Mining (EXPEDO) 2 Mining for Blocks Fuzzy and Crisp Blocks Generating Blocks Managing Hierarchies Visualizing Blocks 3 Multiple-Level Multidimensional Sequential Patterns Multidimensional Sequential Patterns Multiple Level MSP Implementation 4 Conclusion and perspectives EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  4. 4. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives OLAP and KDD OLTP vs. OLAP OLAP Users Decision makers Complex Queries Current Uses OLAP framework : mainly provides navigation and reporting tools (pull) Need for Data Mining (push) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  5. 5. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives OLAP and KDD OLAP Mining First introduced in 1997 by Jiawei Han as a mechanism which integrates OLAP with data mining so that mining can be performed in different portions of databases or data warehouses and at different levels of abstraction at user’s finger tips EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  6. 6. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives OLAP and KDD Specificities of the OLAP Framework On-line analysis measures described by means of dimensions aggregated measure values hierarchies displaying data : the order matters (switch, pivot) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  7. 7. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives OLAP and KDD Motivating Example Beer Water Soda Wine Milk Europe 4 4 7 6 5 America 4 5 7 7 6 Asia 3 3 6 5 5 Africa 2 2 6 5 4 Beer Water Milk Wine Soda America 4 5 6 7 7 Europe 4 4 5 6 7 Asia 3 3 5 5 6 Africa 2 2 4 5 6 EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  8. 8. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives OLAP and KDD Representing Cubes Several Ways to represent the same data Finding the best representations is known as being NP-Hard EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  9. 9. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Hierarchies Using Hierarchies Representativity of extracted Knowledge high : nothing can be extracted (and trivial knowledge) low : too many patterns extracted, no use for the decision makers Difficulty to choose the best level of granularity to get relevant knowledge Taking Hierarchies into account Extracting rules at different levels of hierarchies Subrules are automatically discovered (thanks to anti-monotonicity) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  10. 10. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Research Topics on OLAP Mining (EXPEDO) Research Topics from EXPEDO Topics addressed by the project Mining for Rules (e.g. association rules, gradual rules, sequential patterns) Mining for homogeneous parts and compressing (e.g. blocks) Navigating by means of intelligent queries To be addressed in this talk Mining for Blocks Mining for Multidimensional Sequential Patterns EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  11. 11. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Outline 1 Introduction OLAP and Data Mining Research Topics on OLAP Mining (EXPEDO) 2 Mining for Blocks Fuzzy and Crisp Blocks Generating Blocks Managing Hierarchies Visualizing Blocks 3 Multiple-Level Multidimensional Sequential Patterns Multidimensional Sequential Patterns Multiple Level MSP Implementation 4 Conclusion and perspectives EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  12. 12. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Why Blocks ? Impossibe to Mine for the Best Representation Different kinds of relevant representations Other criteria may be considered : pointing out homogeneous parts What are Blocks ? Blocks are subcubes defined over all dimensions some dimensions may appear completely : ALL level Blocks must be large enough (Support) Blocks must be homogeneous enough (Confidence) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  13. 13. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives PRODUCT 111 000 111 000 111111 000000 5 P1 111 000 6 111 000 111 000 111111 000000 86 5 111111 000000 2 111 000 111 000 111 000 111 000 111111 000000 111111 000000 6 75 111 000 P2 6 111 000 111 000 8 111111 000000 5 5 111111 000000 1111111111 0000000000 111111 000000 1111111111 1111111111 0000000000 0000000000 111111 000000 2 1111111111 1111111111 0000000000 0000000000 P3 8 5 111111 000000 5 2 8 1111111111 1111111111 0000000000 0000000000 1111111111 1111111111 0000000000 0000000000 1111111111 1111111111 0000000000 0000000000 P4 1111111111 1111111111 0000000000 0000000000 8 8 8 1111111111 0000000000 2 2 2 C1 C2 C3 C4 C5 C6 CITY Block Value the number of measure values may be numerous, thus preventing from discovering blocks EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  14. 14. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Fuzzy and Crisp Blocks Partitioning the measure : Crisp Blocks 6 5.9 7.8 4.8 5 0 10 6.1 8 5.1 4.7 5.3 8.1 5 4.9 2.4 1.8 7.9 8.1 8.2 2.2 1.9 0 2 5 8 10 EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  15. 15. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Fuzzy and Crisp Blocks Partitioning the measure : Fuzzy Blocks 6 5.9 7.8 4.8 5 0 10 6.1 8 5.1 4.7 5.3 8.1 5 4.9 2.4 1.8 7.9 8.1 8.2 2.2 1.9 0 2 5 8 10 EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  16. 16. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Generating Blocks Given a k -dimensional cube C, a support threshold σ and a confidence threshold γ. For each measure values m : 1 For every dimension, compute all maximal intervals of values containing enough matching measure values m 2 Combine the intervals in a level wise manner 3 Considering the set of all blocks computed in the previous step, sort out those that are not minimal with respect to the inclusion ordering and then those having a confidence for m less than or equal to γ. EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  17. 17. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Example #8 3 2 1 0 PRODUCT P1 6 6 8 5 5 2 P2 6 8 5 5 6 75 P3 8 5 5 2 2 8 P4 8 8 8 2 2 2 C1 C2 C3 C4 C5 C6 #8 2 1 0 EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  18. 18. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Problem On real data : it may be the case that no (or very few) slice is relevant regarding the support : not enough cell with the measure value being considered Alternatives : Decrease the minimum support value Merge slices Note that, in this case, considering hierarchies leads to semantically-founded merged slices Note that the support must remain anti-monotonic EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  19. 19. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Managing Hierarchies 12.8 12 12.8 12 6 6 ity ity C C th 14 14 5 5 or ity ity N C C 6.2 18 4.8 6.2 18 4.8 4 4 ity ity ity ity C C 4.1 8.3 4.1 8.3 3 3 C C 8.2 8.2 h 2.9 17 8.4 2.9 17 8.4 2 2 ut ity ity So C C 1 8 7.9 7.8 1 8 7.9 7.8 ity ity C C r d er tte da r d ea er te da ea at bu at so t br w bu so br w Beverage Food EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  20. 20. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Visualizing Blocks Visualizing Blocks Users can only have 2D (possibly 3D) visions of their data And thus use projections over some values on the remaining invisible dimensions ... ... But they are interested to know about the rest − > Coloring cells depending on the block informations EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  21. 21. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Visualizing Blocks Example EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  22. 22. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Visualizing Blocks INTERLUDE INTERLUDE on SEQUENTIAL PATTERNS EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  23. 23. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Outline 1 Introduction OLAP and Data Mining Research Topics on OLAP Mining (EXPEDO) 2 Mining for Blocks Fuzzy and Crisp Blocks Generating Blocks Managing Hierarchies Visualizing Blocks 3 Multiple-Level Multidimensional Sequential Patterns Multidimensional Sequential Patterns Multiple Level MSP Implementation 4 Conclusion and perspectives EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  24. 24. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Sequential Patterns Relations between events Partial Order on the data (e.g. temporal order) Many applications : marketing-CRM, decision making, bioinformatics, . . . §Sequential Patterns only use a small part of the data available (single dimension) What about the other dimensions ? EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  25. 25. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Goal : Combining several dimensions in the patterns an item from a sequence is defined over several dimensions EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  26. 26. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Goal : Combining several dimensions in the patterns an item from a sequence is defined over several dimensions classical item : c EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  27. 27. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Goal : Combining several dimensions in the patterns an item from a sequence is defined over several dimensions classical item : c multidimensional item : (Pakistan, c) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  28. 28. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Goal : Combining several dimensions in the patterns an item from a sequence is defined over several dimensions classical item : c multidimensional item : (Pakistan, c) multidimensional sequence : {(Pakistan, carpet1), (Pakistan, pashmina1)}{(France, carpet1)} instead of simply (carpet1, pashmina1), carpet1 Warning : clients are usually groups EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  29. 29. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multidimensional Sequential Patterns Data Model : Blocks again ! DB partitioned into blocks regarding some dimensions, e.g. customer group. A block is considered as a client. BlocID Date Place Product 1 January Pakistan c1 1 January Pakistan c2 1 January Pakistan p1 1 March France c1 1 March Pakistan c1 2 February UK p2 2 June Pakistan c1 2 June Pakistan p2 2 July France c1 3 April Pakistan p1 3 April Pakistan c1 3 September France c1 EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  30. 30. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multidimensional Sequential Patterns Dimension Partition D = DF ⊕ DR ⊕ DA ⊕ Dt Dt : temporal dimensions DA : analysis dimensions DR : reference dimensions DF : forgotten dimensions EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  31. 31. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multidimensional Sequential Patterns Support of a sequence considering a particular sequence ς support computed over reference dimensions Support of ς number of blocks supporting ς support(ς) = number of blocks EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  32. 32. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multidimensional Sequential Patterns Example DR = {Bid }, DA = {Place, Product} and DT = {Date}, minsupp = 2 Compute support of ς = {(Pakistan, c1), (Pakistan, p1)}{(France, c1)} EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  33. 33. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multidimensional Sequential Patterns ς = {(Pakistan, c1), (Pakistan, p1)}{(France, c1)} Block 1 1 January Pakistan c1 1 January Pakistan c2 1 January Pakistan p1 1 March France c1 1 March Pakistan c1 Block 1 supports ς : support(ς) + + Block 2 Block 2 does not support ς : 2 February UK p2 2 June Pakistan c1 2 June Pakistan p2 2 July France c1 EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  34. 34. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multidimensional Sequential Patterns ς = {(Pakistan, c1), (Pakistan, p1)}{(France, c1)} block 3 3 April Pakistan p1 3 April Pakistan c1 3 2 France c1 block 3 supports ς : support(ς) + + support(ς) = 2 ≥ minsupp ς is frequent EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  35. 35. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multiple Level MSP Need for Hierarchies what if minsup=3 ? ς would not have been frequent Using all the dimensions at the lowest granularity level may lead to ... nothing then necessary to consider aggregation the dimension may then be rolled up, or simply ignored (ALL level) ς = {(Pakistan, c1), (Pakistan, pashmina)}{(France, c1)} EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  36. 36. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multiple Level MSP Managing Hierarchies describing hierarchies between elements only the leaves can appear in the DB Example of hierarchies on dimensions PRODUCT : Product (ALL) carpet pashmina c1 c2 ... p1 p2 ... ... EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  37. 37. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multiple Level MSP Agrawal & Srikant (1996) : taking hierachies into account. the database is rewritten by putting the items together with their ancestors (not possible if several dimensions with several levels) J. Han (2001) : Extraction of knowledge level by level but the mined knowledge concerns only one level at one time Choong et al. (2005) : many dimension appear in the patterns they can appear at any granularity level EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  38. 38. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multiple Level MSP Item, itemset and h-generalized sequences Multidimensional h-generalized Item : tuple with labels taken at any ganularity level Examples : (Pakistan, c2) (EU, p1) (EU, pashmina) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  39. 39. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multiple Level MSP Item, itemset and h-generalized sequences Multidimensional h-generalized Item : tuple with labels taken at any ganularity level Examples : (Pakistan, c2) (EU, p1) (EU, pashmina) Hierarchical Inclusion Given e = (d1 , . . . , dm ) and e = (d1 , . . . , dm ), e can be : more general than e (e >h e ) more specific than e (e <h e ) not comparable to e (e h e and e h e) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  40. 40. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multiple Level MSP Example : (Pakistan, carpet) >h (Pakistan, c1). (France, c4) <h (EU, carpet). (EU, c1) and (France, carpet) are not comparable (France, c1) and (Pakistan, c1) are not comparable EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  41. 41. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multiple Level MSP Itemset and h-generalized sequence h-generalized Itemset : set of non comparable items {(France, c1), (USA, p1)} YES {(France, c1), (France, carpet)} NO because (France, c1) <h (France, carpet) Multidimensional h-generalized Sequence s = i1 , . . . , ij is an ordered non empty list of multidimensional h-generalized itemsets. {(India, c1), (Pakistan, c1)}{(EU, c1)} EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  42. 42. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Multiple Level MSP Support Item supported by a transaction A transaction supports an item e if the value is equal to e or under e in the hierarchy (Block _1, February , France, c1) supports the item (EU, carpet). sequence supported by a block A block supports a sequence if all itemsets are supported (provided that the order is respected) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  43. 43. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Implementation Algorithms Generation of candidate items extract all the maximally specific items levelwise generation Generation of candidate sequences anti-monotonicity of the support Apriori like approach (generate - prune) Use of a prefix tree to store candidate sequences (PSP) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  44. 44. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Implementation Experiments Indicators time consumption memory consumption number of patterns being discovered Data synthetic data real data (examples from our industrial collaborations) National French Electricity Agency (marketing) Follow-up of long term care patients to improve facilities e-couponing (customized coupons sent to mobile phones) EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  45. 45. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Outline 1 Introduction OLAP and Data Mining Research Topics on OLAP Mining (EXPEDO) 2 Mining for Blocks Fuzzy and Crisp Blocks Generating Blocks Managing Hierarchies Visualizing Blocks 3 Multiple-Level Multidimensional Sequential Patterns Multidimensional Sequential Patterns Multiple Level MSP Implementation 4 Conclusion and perspectives EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  46. 46. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Conclusion Summary OLAP Mining must not only plug data mining on top of multidimensional databases without any consideration of the specificities Discovery of homogeneous parts Hierarchy-aware Multidimensional Sequential Patterns Work has also been done on fuzzy sequential patterns EXPEDO LIRMM-ETIS-HELP UC OLAP Mining
  47. 47. Introduction Mining for Blocks Multiple-Level Multidimensional Sequential Patterns Conclusion and perspectives Further Work Next Challenges Causality Outliers Support counting and measure values Condensed representations Gradual Rules (generalization of the ordered/temporal dimension), Enrol the user Test on real data EXPEDO LIRMM-ETIS-HELP UC OLAP Mining

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