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Carnegie Mellon Univ. Dept. of Computer Science 15-415/615 – DB Applications

Carnegie Mellon Univ. Dept. of Computer Science 15-415/615 – DB Applications. Lecture # 24: Data Warehousing / Data Mining (R&G, ch 25 and 26). Data mining - detailed outline. Problem Getting the data: Data Warehouses, DataCubes, OLAP Supervised learning: decision trees

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Carnegie Mellon Univ. Dept. of Computer Science 15-415/615 – DB Applications

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  1. Carnegie Mellon Univ.Dept. of Computer Science15-415/615 – DB Applications Lecture # 24: Data Warehousing / Data Mining (R&G, ch 25 and 26)

  2. Data mining - detailed outline • Problem • Getting the data: Data Warehouses, DataCubes, OLAP • Supervised learning: decision trees • Unsupervised learning • association rules • (clustering) CMU SCS 15-415/615

  3. PGH NY sales(p-id, c-id, date, $price) ??? customers( c-id, age, income, ...) SF Problem Given: multiple data sources Find: patterns (classifiers, rules, clusters, outliers...) CMU SCS 15-415/615

  4. Data Ware-housing First step: collect the data, in a single place (= Data Warehouse) How? How often? How about discrepancies / non-homegeneities? CMU SCS 15-415/615

  5. Data Ware-housing First step: collect the data, in a single place (= Data Warehouse) How? A: Triggers/Materialized views How often? A: [Art!] How about discrepancies / non-homegeneities? A: Wrappers/Mediators CMU SCS 15-415/615

  6. Data Ware-housing Step 2: collect counts. (DataCubes/OLAP) Eg.: CMU SCS 15-415/615

  7. OLAP Problem: “is it true that shirts in large sizes sell better in dark colors?” sales ... CMU SCS 15-415/615

  8. f size color color; size DataCubes ‘color’, ‘size’: DIMENSIONS ‘count’: MEASURE CMU SCS 15-415/615

  9. DataCubes ‘color’, ‘size’: DIMENSIONS ‘count’: MEASURE f size color color; size CMU SCS 15-415/615

  10. DataCubes ‘color’, ‘size’: DIMENSIONS ‘count’: MEASURE f size color color; size CMU SCS 15-415/615

  11. DataCubes ‘color’, ‘size’: DIMENSIONS ‘count’: MEASURE f size color color; size CMU SCS 15-415/615

  12. DataCubes ‘color’, ‘size’: DIMENSIONS ‘count’: MEASURE f size color color; size CMU SCS 15-415/615

  13. DataCubes ‘color’, ‘size’: DIMENSIONS ‘count’: MEASURE f size color color; size DataCube CMU SCS 15-415/615

  14. DataCubes SQL query to generate DataCube: • Naively (and painfully:) select size, color, count(*) from sales where p-id = ‘shirt’ group by size, color select size, count(*) from sales where p-id = ‘shirt’ group by size ... CMU SCS 15-415/615

  15. DataCubes SQL query to generate DataCube: • with ‘cube by’ keyword: select size, color, count(*) from sales where p-id = ‘shirt’ cube by size, color CMU SCS 15-415/615

  16. DataCubes DataCube issues: Q1: How to store them (and/or materialize portions on demand) Q2: Which operations to allow CMU SCS 15-415/615

  17. DataCubes DataCube issues: Q1: How to store them (and/or materialize portions on demand) A: ROLAP/MOLAP Q2: Which operations to allow A: roll-up, drill down, slice, dice [More details: book by Han+Kamber] CMU SCS 15-415/615

  18. DataCubes Q1: How to store a dataCube? CMU SCS 15-415/615

  19. DataCubes Q1: How to store a dataCube? A1: Relational (R-OLAP) CMU SCS 15-415/615

  20. DataCubes Q1: How to store a dataCube? A2: Multi-dimensional (M-OLAP) A3: Hybrid (H-OLAP) CMU SCS 15-415/615

  21. DataCubes Pros/Cons: ROLAP strong points: (DSS, Metacube) CMU SCS 15-415/615

  22. DataCubes Pros/Cons: ROLAP strong points: (DSS, Metacube) • use existing RDBMS technology • scale up better with dimensionality CMU SCS 15-415/615

  23. DataCubes Pros/Cons: MOLAP strong points: (EssBase/hyperion.com) • faster indexing (careful with: high-dimensionality; sparseness) HOLAP: (MS SQL server OLAP services) • detail data in ROLAP; summaries in MOLAP CMU SCS 15-415/615

  24. DataCubes Q1: How to store a dataCube Q2: What operations should we support? CMU SCS 15-415/615

  25. f size color color; size DataCubes Q2: What operations should we support? CMU SCS 15-415/615

  26. DataCubes Q2: What operations should we support? Roll-up f size color color; size CMU SCS 15-415/615

  27. DataCubes Q2: What operations should we support? Drill-down f size color color; size CMU SCS 15-415/615

  28. DataCubes Q2: What operations should we support? Slice f size color color; size CMU SCS 15-415/615

  29. DataCubes Q2: What operations should we support? Dice f size color color; size CMU SCS 15-415/615

  30. DataCubes Q2: What operations should we support? • Roll-up • Drill-down • Slice • Dice • (Pivot/rotate; drill-across; drill-through • top N • moving averages, etc) CMU SCS 15-415/615

  31. D/W - OLAP - Conclusions • D/W: copy (summarized) data + analyze • OLAP - concepts: • DataCube • R/M/H-OLAP servers • ‘dimensions’; ‘measures’ CMU SCS 15-415/615

  32. Outline • Problem • Getting the data: Data Warehouses, DataCubes, OLAP • Supervised learning: decision trees • Unsupervised learning • association rules • (clustering) CMU SCS 15-415/615

  33. Decision trees - Problem ?? CMU SCS 15-415/615

  34. num. attr#2 (eg., chol-level) - - + + + - + - + - + - + num. attr#1 (eg., ‘age’) Decision trees • Pictorially, we have CMU SCS 15-415/615

  35. Decision trees • and we want to label ‘?’ ? num. attr#2 (eg., chol-level) - - + + + - + - + - + - + num. attr#1 (eg., ‘age’) CMU SCS 15-415/615

  36. Decision trees • so we build a decision tree: ? num. attr#2 (eg., chol-level) - - + + + 40 - + - + - + - + 50 num. attr#1 (eg., ‘age’) CMU SCS 15-415/615

  37. Decision trees • so we build a decision tree: age<50 N Y chol. <40 + Y N - ... CMU SCS 15-415/615

  38. Outline • Problem • Getting the data: Data Warehouses, DataCubes, OLAP • Supervised learning: decision trees • problem • approach • scalability enhancements • Unsupervised learning • association rules • (clustering) CMU SCS 15-415/615

  39. Decision trees • Typically, two steps: • tree building • tree pruning (for over-training/over-fitting) CMU SCS 15-415/615

  40. num. attr#2 (eg., chol-level) - - + + + - + - + - + - + num. attr#1 (eg., ‘age’) Tree building • How? CMU SCS 15-415/615

  41. Tree building • How? • A: Partition, recursively - pseudocode: Partition ( Dataset S) if all points in S have same label then return evaluate splits along each attribute A pick best split, to divide S into S1 and S2 Partition(S1); Partition(S2) CMU SCS 15-415/615

  42. Not In Exam=N.I.E. Tree building • Q1: how to introduce splits along attribute Ai • Q2: how to evaluate a split? CMU SCS 15-415/615

  43. N.I.E. Tree building • Q1: how to introduce splits along attribute Ai • A1: • for num. attributes: • binary split, or • multiple split • for categorical attributes: • compute all subsets (expensive!), or • use a greedy algo CMU SCS 15-415/615

  44. N.I.E. Tree building • Q1: how to introduce splits along attribute Ai • Q2: how to evaluate a split? CMU SCS 15-415/615

  45. N.I.E. Tree building • Q1: how to introduce splits along attribute Ai • Q2: how to evaluate a split? • A: by how close to uniform each subset is - ie., we need a measure of uniformity: CMU SCS 15-415/615

  46. N.I.E. Tree building entropy: H(p+, p-) Any other measure? 1 0 0.5 0 1 p+ CMU SCS 15-415/615

  47. N.I.E. 1 0 0.5 0 1 p+ Tree building entropy: H(p+, p-) ‘gini’ index: 1-p+2 - p-2 1 0 0.5 0 1 p+ CMU SCS 15-415/615

  48. N.I.E. Tree building entropy: H(p+, p-) ‘gini’ index: 1-p+2 - p-2 (How about multiple labels?) CMU SCS 15-415/615

  49. N.I.E. Tree building Intuition: • entropy: #bits to encode the class label • gini: classification error, if we randomly guess ‘+’ with prob. p+ CMU SCS 15-415/615

  50. N.I.E. num. attr#2 (eg., chol-level) - - + + + - + - + - + - + num. attr#1 (eg., ‘age’) Tree building Thus, we choose the split that reduces entropy/classification-error the most: Eg.: CMU SCS 15-415/615

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