04 data mining : data generelization
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Chapter 4 of 'Data Mining: Concepts and Techniques' discusses efficient computation methods for data cubes and data generalization. It covers techniques such as multi-way array aggregation, bottom-up computation, and integrates various approaches for effective cube computation. It also addresses challenges like high-dimensional OLAP and the 'curse of dimensionality,' presenting strategies for managing large datasets.
![Comparisons to Related Work
[Harinarayan96] computes low-dimensional cuboids by
further aggregation of high-dimensional cuboids.
Opposite of our method’s direction.
Inverted indexing structures [Witten99] focus on single
dimensional data or multi-dimensional data with no
aggregation.
Tree-stripping [Berchtold00] uses similar vertical
partitioning of database but no aggregation.
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Data Mining: Concepts and Techniques
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![Presentation of Generalized Results
Generalized relation:
Relations where some or all attributes are generalized, with counts
or other aggregation values accumulated.
Cross tabulation:
Mapping results into cross tabulation form (similar to contingency
tables).
Visualization techniques:
Pie charts, bar charts, curves, cubes, and other visual forms.
Quantitative characteristic rules:
Mapping generalized result into characteristic rules with quantitative
information associated with it, e.g.,
grad ( x) ∧ male( x) ⇒
birth _ region( x) ="Canada"[t :53%]∨ birth _ region( x) =" foreign"[t : 47%].
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![Quantitative Discriminant Rules
Cj = target class
qa = a generalized tuple covers some tuples of class
but can also cover some tuples of contrasting class
d-weight
count(qa ∈Cj )
range: [0, 1]
d − weight =
m
∑count(q
a
∈Ci )
i =1
quantitative discriminant rule form
∀ X, target_class(X) ⇐ condition(X) [d : d_weight]
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![Example: Quantitative Discriminant Rule
Status
Birth_country
Age_range
Gpa
Count
Graduate
Canada
25-30
Good
90
Undergraduate
Canada
25-30
Good
210
Count distribution between graduate and undergraduate students for a generalized tuple
Quantitative discriminant rule
∀ X , graduate _ student ( X ) ⇐
birth _ country ( X ) =" Canada"∧ age _ range( X ) ="25 − 30"∧ gpa ( X ) =" good " [d : 30%]
where 90/(90 + 210) = 30%
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![Class Description
Quantitative characteristic rule
∀ X, target_class(X) ⇒ condition(X) [t : t_weight]
necessary
Quantitative discriminant rule
∀ X, target_class(X) ⇐ condition(X) [d : d_weight]
sufficient
Quantitative description rule
∀ X, target_class(X) ⇔
condition 1(X) [t : w1, d : w ′1] ∨ ... ∨ conditionn(X) [t : wn, d : w ′n]
necessary and sufficient
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![Example: Quantitative Description Rule
Location/item
TV
Computer
Both_items
Count
t-wt
d-wt
Count
t-wt
d-wt
Count
t-wt
d-wt
Europe
80
25%
40%
240
75%
30%
320
100%
32%
N_Am
120
17.65%
60%
560
82.35%
70%
680
100%
68%
Both_
regions
200
20%
100%
800
80%
100%
1000
100%
100%
Crosstab showing associated t-weight, d-weight values and total number
(in thousands) of TVs and computers sold at AllElectronics in 1998
Quantitative description rule for target class Europe
∀ X, Europe(X) ⇔
(item(X) =" TV" ) [t : 25%, d : 40%] ∨ (item(X) =" computer" ) [t : 75%, d : 30%]
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04 data mining : data generelization
- 1. Data Mining: Concepts and Techniques — Chapter 4 — Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign www.cs.uiuc.edu/~hanj ©2006 Jiawei Han and Micheline Kamber, All rights reserved 12/26/13 Data Mining: Concepts and Techniques 1
- 2. 12/26/13 Data Mining: Concepts and Techniques 2
- 3. Chapter 4: Data Cube Computation and Data Generalization Efficient Computation of Data Cubes Exploration and Discovery in Multidimensional Databases Attribute-Oriented Induction ─ An Alternative Data Generalization Method 12/26/13 Data Mining: Concepts and Techniques 3
- 4. Efficient Computation of Data Cubes Preliminary cube computation tricks (Agarwal et al.’96) Computing full/iceberg cubes: 3 methodologies Top-Down: Multi-Way array aggregation (Zhao, Deshpande & Naughton, SIGMOD’97) Bottom-Up: H-cubing technique (Han, Pei, Dong & Wang: SIGMOD’01) Integrating Top-Down and Bottom-Up: Bottom-up computation: BUC (Beyer & Ramarkrishnan, SIGMOD’99) Star-cubing algorithm (Xin, Han, Li & Wah: VLDB’03) High-dimensional OLAP: A Minimal Cubing Approach (Li, et al. VLDB’04) Computing alternative kinds of cubes: Partial cube, closed cube, approximate cube, etc. 12/26/13 Data Mining: Concepts and Techniques 4
- 5. Preliminary Tricks (Agarwal et al. VLDB’96) Sorting, hashing, and grouping operations are applied to the dimension attributes in order to reorder and cluster related tuples Aggregates may be computed from previously computed aggregates, rather than from the base fact table Smallest-child: computing a cuboid from the smallest, previously computed cuboid Cache-results: caching results of a cuboid from which other cuboids are computed to reduce disk I/Os Amortize-scans: computing as many as possible cuboids at the same time to amortize disk reads Share-sorts: sharing sorting costs cross multiple cuboids when sort-based method is used Share-partitions: sharing the partitioning cost across multiple cuboids when hash-based algorithms are used 12/26/13 Data Mining: Concepts and Techniques 5
- 6. Multi-Way Array Aggregation Array-based “bottom-up” algorithm Using multi-dimensional chunks No direct tuple comparisons Simultaneous aggregation on multiple dimensions Intermediate aggregate values are re-used for computing ancestor cuboids Cannot do Apriori pruning: No iceberg optimization 12/26/13 a ll A B A B C A C A BC Data Mining: Concepts and Techniques 6 BC
- 7. Multi-way Array Aggregation for Cube Computation (MOLAP) Partition arrays into chunks (a small subcube which fits in memory). Compressed sparse array addressing: (chunk_id, offset) Compute aggregates in “multiway” by visiting cube cells in the order which minimizes the # of times to visit each cell, and reduces memory access and storage cost. C c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 b3 b2 9 b1 5 b0 B B 13 14 15 16 1 2 3 4 a0 a1 a2 a3 12/26/13 A 60 44 28 56 40 24 52 36 20 What is the best traversing order to do multi-way aggregation? Data Mining: Concepts and Techniques 7
- 8. Multi-way Array Aggregation for Cube Computation C c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 b3 B b2 B 13 14 15 16 28 9 24 b1 5 b0 1 2 3 4 a0 a1 a2 44 40 60 56 52 a3 20 36 A 12/26/13 Data Mining: Concepts and Techniques 8
- 9. Multi-way Array Aggregation for Cube Computation C c3 61 62 63 64 c2 45 46 47 48 c1 29 30 31 32 c0 b3 B b2 B 13 14 15 16 28 9 24 b1 5 b0 1 2 3 4 a0 a1 a2 44 40 60 56 52 a3 20 36 A 12/26/13 Data Mining: Concepts and Techniques 9
- 10. Multi-Way Array Aggregation for Cube Computation (Cont.) Method: the planes should be sorted and computed according to their size in ascending order Idea: keep the smallest plane in the main memory, fetch and compute only one chunk at a time for the largest plane Limitation of the method: computing well only for a small number of dimensions If there are a large number of dimensions, “top-down” computation and iceberg cube computation methods can be explored 12/26/13 Data Mining: Concepts and Techniques 10
- 11. Bottom-Up Computation (BUC) BUC (Beyer & Ramakrishnan, SIGMOD’99) Bottom-up cube computation (Note: top-down in our view!) Divides dimensions into partitions and facilitates iceberg pruning If a partition does not satisfy min_sup, its descendants can be pruned 3 A B If minsup = 1 ⇒ compute full CUBE! 4 A BC No simultaneous aggregation A B A B C 12/26/13 a ll A A C B C A D A B D D B C C D B D A C D B C D A B C D 1 a ll 2 A 7 A C 6 A BD 10 B 9 A D 14 C 11 BC 8 A C D 16 D 13 BD 12 BC D 5 BC D Data Mining: Concepts andA Techniques 11 15 C D
- 12. BUC: Partitioning Usually, entire data set fit in main memory can’t Sort distinct values, partition into blocks that fit Continue processing Optimizations Partitioning Ordering dimensions to encourage pruning External Sorting, Hashing, Counting Sort Cardinality, Skew, Correlation Collapsing duplicates Can’t do holistic aggregates anymore! 12/26/13 Data Mining: Concepts and Techniques 12
- 13. H-Cubing: Using H-Tree Structure a ll Bottom-up computation Exploring an H-tree structure If the current computation of an H-tree cannot pass min_sup, do not proceed further (pruning) A A B A B C B A C A B D A D A C D C B C D B D B C D A B C D No simultaneous aggregation 12/26/13 Data Mining: Concepts and Techniques 13 C D
- 14. H-tree: A Prefix Hyper-tree Header table Attr. Val. Edu Hhd Bus … Jan Feb … Tor Van Mon … Quant-Info Sum:2285 … … … … … … … … … … … Side-link root Jan Tor Month City Cust_grp Prod Cost Tor Edu Printer 500 485 Jan Tor Hhd TV 800 1200 Jan Tor Edu Camera 1160 1280 Feb Mon Bus Laptop 1500 2500 Mar Van Edu HD 540 520 … … … Mar Jan Feb Van Tor Mon Q.I. Q.I. Q.I. Price Jan bus hhd edu … … 12/26/13 Quant-Info Sum: 1765 Cnt: 2 bins Data … Mining: Concepts and Techniques 14
- 15. H-Cubing: Computing Cells Involving Dimension City Header Table HTor Attr. Val. Edu Hhd Bus … Jan Feb … Tor Van Mon … 12/26/13 Attr. Val. Edu Hhd Bus … Jan Feb … Quant-Info Sum:2285 … … … … … … … … … … … Sidelink Q.I. … … … … … … … From (*, *, Tor) to (*, Jan, Tor) root Hhd. Edu. Jan. Side-link Tor. Quant-Info Mar. Jan. Feb. Van. Tor. Mon. Q.I. Q.I. Q.I. Sum: 1765 Cnt: 2 bins Bus. Data Mining: Concepts and Techniques 15
- 16. Computing Cells Involving Month But No City 1. Roll up quant-info 2. Compute cells involving month but no city Attr. Val. Edu. Hhd. Bus. … Jan. Feb. Mar. … Tor. Van. Mont. … 12/26/13 Quant-Info Sum:2285 … … … … … … … … … … … … Side-link root Hhd. Edu. Bus. Jan. Jan. Feb. Q.I. Tor. Mar. Q.I. Q.I. Q.I. Van. Tor. Mont. Top-k OK mark: if Q.I. in a child passes top-k avg threshold, so does its parents. No binning is needed! Data Mining: Concepts and Techniques 16
- 17. Computing Cells Involving Only Cust_grp root Check header table directly Attr. Val. Edu Hhd Bus … Jan Feb Mar … Tor Van Mon … 12/26/13 Quant-Info Sum:2285 … … … … … … … … … … … … Side-link hhd edu bus Jan Jan Feb Q.I. Tor Mar Q.I. Q.I. Q.I. Van Tor Data Mining: Concepts and Techniques 17 Mon
- 18. Star-Cubing: An Integrating Method Integrate the top-down and bottom-up methods Explore shared dimensions E.g., dimension A is the shared dimension of ACD and AD ABD/AB means cuboid ABD has shared dimensions AB Allows for shared computations e.g., cuboid AB is computed simultaneously as ABD Aggregate in a top-down manner but with the bottom-up sub-layer underneath which will allow Apriori pruning C /C D Shared dimensions grow in bottom-up fashion A C /A C A B C /A B C A D /A A B D /A B B C /B C A C D /A B D /B BC D A B C D /a ll 12/26/13 Data Mining: Concepts and Techniques 18 C D
- 19. Iceberg Pruning in Shared Dimensions Anti-monotonic property of shared dimensions If the measure is anti-monotonic, and if the aggregate value on a shared dimension does not satisfy the iceberg condition, then all the cells extended from this shared dimension cannot satisfy the condition either Intuition: if we can compute the shared dimensions before the actual cuboid, we can use them to do Apriori pruning Problem: how to prune while still aggregate simultaneously on multiple dimensions? 12/26/13 Data Mining: Concepts and Techniques 19
- 20. Cell Trees Use a tree structure similar to H-tree to represent cuboids Collapses common prefixes to save memory Keep count at node Traverse the tree to retrieve a particular tuple 12/26/13 Data Mining: Concepts and Techniques 20
- 21. Star Attributes and Star Nodes Intuition: If a single-dimensional aggregate on an attribute value p does not satisfy the iceberg condition, it is useless to distinguish them during the iceberg computation E.g., b2, b3, b4, c1, c2, c4, d1, d2, d3 Solution: Replace such attributes by a *. Such attributes are star attributes, and the corresponding nodes in the cell tree are star nodes 12/26/13 A B C D Count a1 b1 c1 d1 1 a1 b1 c4 d3 1 a1 b2 c2 d2 1 a2 b3 c3 d4 1 a2 b4 c3 d4 1 Data Mining: Concepts and Techniques 21
- 22. Example: Star Reduction Suppose minsup = 2 Perform one-dimensional aggregation. Replace attribute values whose count < 2 with *. And collapse all *’s together Resulting table has all such attributes replaced with the starattribute A B C D Count a1 b1 * * 1 a1 b1 * * 1 a1 * * * 1 a2 * c3 d4 1 a2 * c3 d4 1 A B C D Count With regards to the iceberg a1 b1 * * 2 computation, this new table is a a1 * * * 1 loseless compression of the original a2 * c3 d4 2 table 12/26/13 Data Mining: Concepts and Techniques 22
- 23. Star Tree Given the new compressed table, it is possible to construct the corresponding cell tree— called star tree Keep a star table at the side for easy lookup of star attributes The star tree is a loseless compression of the original cell tree 12/26/13 Data Mining: Concepts and Techniques 23
- 24. Star-Cubing Algorithm—DFS on Lattice Tree a ll BC D : 51 b*: 33 A /A B /B C /C b1: 26 D /D ro o t: 5 c*: 14 c3: 211 c* : 27 d*: 15 d4 : 2 12 d*: 28 A B /A B A B C /A B C A C /A C A D /A A B D /A B B C /B C A C D /A B D /B C D a1: 3 BC D a2: 2 b*: 1 b1: 2 b*: 2 c*: 1 c*: 2 c3: 2 d*: 1 d*: 2 d4: 2 A BC D 12/26/13 Data Mining: Concepts and Techniques 24
- 25. Multi-Way Aggregation BC D A C D /A A B D /A B A B C /A B C A BC D 12/26/13 Data Mining: Concepts and Techniques 25
- 26. Star-Cubing Algorithm—DFS on Star- Tree 12/26/13 Data Mining: Concepts and Techniques 26
- 27. Multi-Way Star-Tree Aggregation Start depth-first search at the root of the base star tree At each new node in the DFS, create corresponding star tree that are descendents of the current tree according to the integrated traversal ordering E.g., in the base tree, when DFS reaches a1, the ACD/A tree is created When DFS reaches b*, the ABD/AD tree is created The counts in the base tree are carried over to the new trees 12/26/13 Data Mining: Concepts and Techniques 27
- 28. Multi-Way Aggregation (2) When DFS reaches a leaf node (e.g., d*), start backtracking On every backtracking branch, the count in the corresponding trees are output, the tree is destroyed, and the node in the base tree is destroyed Example When traversing from d* back to c*, the a1b*c*/a1b*c* tree is output and destroyed When traversing from c* back to b*, the a1b*D/a1b* tree is output and destroyed When at b*, jump to b1 and repeat similar process 12/26/13 Data Mining: Concepts and Techniques 28
- 29. The Curse of Dimensionality None of the previous cubing method can handle high dimensionality! A database of 600k tuples. Each dimension has cardinality of 100 and zipf of 2. 12/26/13 Data Mining: Concepts and Techniques 29
- 30. Motivation of High-D OLAP Challenge to current cubing methods: The “curse of dimensionality’’ problem Iceberg cube and compressed cubes: only delay the inevitable explosion Full materialization: still significant overhead in accessing results on disk High-D OLAP is needed in applications Science and engineering analysis Bio-data analysis: thousands of genes Statistical surveys: hundreds of variables 12/26/13 Data Mining: Concepts and Techniques 30
- 31. Fast High-D OLAP with Minimal Cubing Observation: OLAP occurs only on a small subset of dimensions at a time Semi-Online Computational Model 1. Partition the set of dimensions into shell fragments 2. Compute data cubes for each shell fragment while retaining inverted indices or value-list indices 3. Given the pre-computed fragment cubes, dynamically compute cube cells of the high12/26/13 Data Mining: Concepts and Techniques 31 dimensional data cube online
- 32. Properties of Proposed Method Partitions the data vertically Reduces high-dimensional cube into a set of lower dimensional cubes Online re-construction of original high-dimensional space Lossless reduction Offers tradeoffs between the amount of pre-processing and the speed of online computation 12/26/13 Data Mining: Concepts and Techniques 32
- 33. Example Computation Let the cube aggregation function be count tid B C D E 1 a1 b1 c1 d1 e1 2 a1 b2 c1 d2 e1 3 a1 b2 c1 d1 e2 4 a2 b1 c1 d1 e2 5 A a2 b1 c1 d1 e3 Divide the 5 dimensions into 2 shell fragments: (A, B, C) and (D, E) 12/26/13 Data Mining: Concepts and Techniques 33
- 34. 1-D Inverted Indices Build traditional invert index or RID list Attribute Value List Size a1 123 3 a2 45 2 b1 145 3 b2 23 2 c1 12345 5 d1 1345 4 d2 2 1 e1 12 2 e2 34 2 e3 12/26/13 TID List 5 1 Data Mining: Concepts and Techniques 34
- 35. Shell Fragment Cubes Generalize the 1-D inverted indices to multi-dimensional ones in the data cube sense Cell TID List List Size a1 b1 123 ∩ 45 1 1 1 a1 b2 123 ∩ 3 2 23 2 a2 b1 4 5 ∩1 4 5 45 2 a2 b2 12/26/13 Intersection 4 5 ∩2 3 ⊗ 0 Data Mining: Concepts and Techniques 35
- 36. Shell Fragment Cubes (2) Compute all cuboids for data cubes ABC and DE while retaining the inverted indices For example, shell fragment cube ABC contains 7 cuboids: A, B, C AB, AC, BC ABC This completes the offline computation stage 12/26/13 Data Mining: Concepts and Techniques 36
- 37. Shell Fragment Cubes (3) Given a database of T tuples, D dimensions, and F shell fragment size, the fragment cubes’ space requirement is: D F OT − ) (2 1 F For F < 5, the growth is sub-linear. 12/26/13 Data Mining: Concepts and Techniques 37
- 38. Shell Fragment Cubes (4) Shell fragments do not have to be disjoint Fragment groupings can be arbitrary to allow for maximum online performance Known common combinations (e.g.,<city, state>) should be grouped together. Shell fragment sizes can be adjusted for optimal balance between offline and online computation 12/26/13 Data Mining: Concepts and Techniques 38
- 39. ID_Measure Table If measures other than count are present, store in ID_measure table separate from the shell fragments tid sum 1 5 70 2 3 10 3 8 20 4 5 40 5 12/26/13 count 2 30 Data Mining: Concepts and Techniques 39
- 40. The Frag-Shells Algorithm 1. Partition set of dimension (A1,…,An) into a set of k fragments (P1, …,Pk). 2. Scan base table once and do the following 3. insert <tid, measure> into ID_measure table. 4. for each attribute value ai of each dimension Ai 5. 6. 7. build inverted index entry <ai, tidlist> For each fragment partition Pi build local fragment cube Si by intersecting tid-lists in bottom- 12/26/13 up fashion. Data Mining: Concepts and Techniques 40
- 41. Frag-Shells (2) D Cuboid EF Cuboid DE Cuboid Dimensions A B C D E F … Cell d1 e1 {2, 4, 6, 7} d2 e1 {5, 10} … 12/26/13 {1, 3, 8, 9} d1 e2 ABC Cube Tuple-ID List … DEF Cube Data Mining: Concepts and Techniques 41
- 42. Online Query Computation A query has the general form Each ai has 3 possible values 1. Aggregate * function 3. Instantiated value 2. a 2K, n:M , a a 1 , Inquire ? function : u cn t For example, 3??*1 oreturns a 2-D data cube. 12/26/13 Data Mining: Concepts and Techniques 42
- 43. Online Query Computation (2) Given the fragment cubes, process a query as follows 1. Divide the query into fragment, same as the shell 2. Fetch the corresponding TID list for each fragment from the fragment cube 3. Intersect the TID lists from each fragment to construct instantiated base table 4. Compute the data cube using the base table with any cubing algorithm 12/26/13 Data Mining: Concepts and Techniques 43
- 44. Online Query Computation (3) A B C D E F G H I Instantiated Base Table 12/26/13 J K L M N … Online Cube Data Mining: Concepts and Techniques 44
- 45. Experiment: Size vs. Dimensionality (50 and 100 cardinality) 12/26/13 (50-C): 106 tuples, 0 skew, 50 cardinality, fragment size 3. (100-C): 106 tuples, 2 skew, 100 cardinality, fragment size 2. Data Mining: Concepts and Techniques 45
- 46. Experiment: Size vs. Shell-Fragment Size 12/26/13 (50-D): 106 tuples, 50 dimensions, 0 skew, 50 cardinality. (100-D): 106 tuples, 100 dimensions, 2 skew, 25 cardinality. Data Mining: Concepts and Techniques 46
- 47. Experiment: Run-time vs. Shell-Fragment Size 12/26/13 106 tuples, 20 dimensions, 10 cardinality, skew 1, fragment size 3, 3 instantiated dimensions. Data Mining: Concepts and Techniques 47
- 48. Experiment: I/O vs. Shell-Fragment Size (10-D): 106 tuples, 10 dimensions, 10 cardinalty, 0 skew, 4 inst., 4 query. (20-D): 106 tuples, 20 dimensions, 10 cardinalty, 1 skew, 3 inst., 4 query. 12/26/13 Data Mining: Concepts and Techniques 48
- 49. Experiment: I/O vs. # of Instantiated Dimensions 12/26/13 106 tuples, 10 dimensions, 10 cardinalty, 0 skew, fragment size 1, 7 total relevant dimensions. Data Mining: Concepts and Techniques 49
- 50. Experiments on Real World Data UCI Forest CoverType data set 54 dimensions, 581K tuples Shell fragments of size 2 took 33 seconds and 325MB to compute 3-D subquery with 1 instantiate D: 85ms~1.4 sec. Longitudinal Study of Vocational Rehab. Data 24 dimensions, 8818 tuples Shell fragments of size 3 took 0.9 seconds and 60MB to compute 5-D query with 0 instantiated D: 227ms~2.6 sec. 12/26/13 Data Mining: Concepts and Techniques 50
- 51. Comparisons to Related Work [Harinarayan96] computes low-dimensional cuboids by further aggregation of high-dimensional cuboids. Opposite of our method’s direction. Inverted indexing structures [Witten99] focus on single dimensional data or multi-dimensional data with no aggregation. Tree-stripping [Berchtold00] uses similar vertical partitioning of database but no aggregation. 12/26/13 Data Mining: Concepts and Techniques 51
- 52. Further Implementation Considerations Incremental Update: Append more TIDs to inverted list Add <tid: measure> to ID_measure table Incremental adding new dimensions Bitmap indexing Form new inverted list and add new fragments May further improve space usage and speed Inverted index compression Store as d-gaps Explore more IR compression methods 12/26/13 Data Mining: Concepts and Techniques 52
- 53. Chapter 4: Data Cube Computation and Data Generalization Efficient Computation of Data Cubes Exploration and Discovery in Multidimensional Databases Attribute-Oriented Induction ─ An Alternative Data Generalization Method 12/26/13 Data Mining: Concepts and Techniques 53
- 54. Computing Cubes with Non-Antimonotonic Iceberg Conditions Most cubing algorithms cannot compute cubes with nonantimonotonic iceberg conditions efficiently Example CREATE CUBE Sales_Iceberg AS SELECT month, city, cust_grp, AVG(price), COUNT(*) FROM Sales_Infor CUBEBY month, city, cust_grp HAVING AVG(price) >= 800 AND COUNT(*) >= 50 Needs to study how to push constraint into the cubing process 12/26/13 Data Mining: Concepts and Techniques 54
- 55. Non-Anti-Monotonic Iceberg Condition Anti-monotonic: if a process fails a condition, continue processing will still fail The cubing query with avg is non-anti-monotonic! (Mar, *, *, 600, 1800) fails the HAVING clause (Mar, *, Bus, 1300, 360) passes the clause Month City Cust_grp Prod Cost Price Jan Tor Edu Printer 500 485 Jan Tor Hld TV 800 1200 Jan Tor Edu Camera 1160 1280 Feb Mon Bus Laptop 1500 2500 Mar Van Edu HD 540 520 … … … … … … 12/26/13 CREATE CUBE Sales_Iceberg AS SELECT month, city, cust_grp, AVG(price), COUNT(*) FROM Sales_Infor CUBEBY month, city, cust_grp HAVING AVG(price) >= 800 AND COUNT(*) >= 50 Data Mining: Concepts and Techniques 55
- 56. From Average to Top-k Average Let (*, Van, *) cover 1,000 records Avg(price) is the average price of those 1000 sales Avg50(price) is the average price of the top-50 sales (top-50 according to the sales price Top-k average is anti-monotonic The top 50 sales in Van. is with avg(price) <= 800 the top 50 deals in Van. during Feb. must be with avg(price) <= 800 Month Cust_grp Prod Cost Price … 12/26/13 City … … … … … Data Mining: Concepts and Techniques 56
- 57. Binning for Top-k Average Computing top-k avg is costly with large k Binning idea Avg50(c) >= 800 Large value collapsing: use a sum and a count to summarize records with measure >= 800 If count>=800, no need to check “small” records Small value binning: a group of bins One bin covers a range, e.g., 600~800, 400~600, etc. Register a sum and a count for each bin 12/26/13 Data Mining: Concepts and Techniques 57
- 58. Computing Approximate top-k average Suppose for (*, Van, *), we have Range Sum Count Over 800 28000 20 600~800 10600 15 400~600 15200 30 … … … Approximate avg50()= (28000+10600+600*15)/50=95 Top 50 2 The cell may pass the HAVING clause Month Cust_grp Prod Cost Price … 12/26/13 City … … … … … Data Mining: Concepts and Techniques 58
- 59. Weakened Conditions Facilitate Pushing Accumulate quant-info for cells to compute average iceberg cubes efficiently Three pieces: sum, count, top-k bins Use top-k bins to estimate/prune descendants Use sum and count to consolidate current cell weakest strongest Approximate avg 50 () real avg 50 () avg() Anti-monotonic, can Anti-monotonic, but Not anti- be computed computationally monotonic efficiently costly 12/26/13 Data Mining: Concepts and Techniques 59
- 60. Computing Iceberg Cubes with Other Complex Measures Computing other complex measures Key point: find a function which is weaker but ensures certain anti-monotonicity Examples Avg() ≤ v: avgk(c) ≤ v (bottom-k avg) Avg() ≥ v only (no count): max(price) ≥ v Sum(profit) (profit can be negative): p_sum(c) ≥ v if p_count(c) ≥ k; or otherwise, sumk(c) ≥ v Others: conjunctions of multiple conditions 12/26/13 Data Mining: Concepts and Techniques 60
- 61. Compressed Cubes: Condensed or Closed Cubes W. Wang, H. Lu, J. Feng, J. X. Yu, Condensed Cube: An Effective Approach to Reducing Data Cube Size, ICDE’02. Icerberg cube cannot solve all the problems Suppose 100 dimensions, only 1 base cell with count = 10. How many aggregate (non-base) cells if count >= 10? Condensed cube Only need to store one cell (a1, a2, …, a100, 10), which represents all the corresponding aggregate cells Adv. Fully precomputed cube without compression Efficient computation of the minimal condensed cube Closed cube Dong Xin, Jiawei Han, Zheng Shao, and Hongyan Liu, “C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking”, ICDE'06. 12/26/13 Data Mining: Concepts and Techniques 61
- 62. Chapter 4: Data Cube Computation and Data Generalization Efficient Computation of Data Cubes Exploration and Discovery in Multidimensional Databases Attribute-Oriented Induction ─ An Alternative Data Generalization Method 12/26/13 Data Mining: Concepts and Techniques 62
- 63. Discovery-Driven Exploration of Data Cubes Hypothesis-driven exploration by user, huge search space Discovery-driven (Sarawagi, et al.’98) Effective navigation of large OLAP data cubes pre-compute measures indicating exceptions, guide user in the data analysis, at all levels of aggregation Exception: significantly different from the value anticipated, based on a statistical model Visual cues such as background color are used to reflect the degree of exception of each cell 12/26/13 Data Mining: Concepts and Techniques 63
- 64. Kinds of Exceptions and their Computation Parameters SelfExp: surprise of cell relative to other cells at same level of aggregation InExp: surprise beneath the cell PathExp: surprise beneath cell for each drill-down path Computation of exception indicator (modeling fitting and computing SelfExp, InExp, and PathExp values) can be overlapped with cube construction Exception themselves can be stored, indexed and retrieved like precomputed aggregates 12/26/13 Data Mining: Concepts and Techniques 64
- 65. Examples: Discovery-Driven Data Cubes 12/26/13 Data Mining: Concepts and Techniques 65
- 66. Complex Aggregation at Multiple Granularities: Multi-Feature Cubes Multi-feature cubes (Ross, et al. 1998): Compute complex queries involving multiple dependent aggregates at multiple granularities Ex. Grouping by all subsets of {item, region, month}, find the maximum price in 1997 for each group, and the total sales among all maximum price tuples select item, region, month, max(price), sum(R.sales) from purchases where year = 1997 cube by item, region, month: R such that R.price = max(price) Continuing the last example, among the max price tuples, find the min and max shelf live, and find the fraction of the total sales due to tuple that have min shelf life within the set of all max price tuples 12/26/13 Data Mining: Concepts and Techniques 66
- 67. Cube-Gradient (Cubegrade) Analysis of changes of sophisticated measures in multidimensional spaces Query: changes of average house price in Vancouver in ‘00 comparing against ’99 Answer: Apts in West went down 20%, houses in Metrotown went up 10% Cubegrade problem by Imielinski et al. Changes in dimensions changes in measures Drill-down, roll-up, and mutation 12/26/13 Data Mining: Concepts and Techniques 67
- 68. From Cubegrade to Multi-dimensional Constrained Gradients in Data Cubes Significantly more expressive than association rules Capture trends in user-specified measures Serious challenges Many trivial cells in a cube “significance constraint” to prune trivial cells Numerate pairs of cells “probe constraint” to select a subset of cells to examine Only interesting changes wanted “gradient constraint” to capture significant changes 12/26/13 Data Mining: Concepts and Techniques 68
- 69. MD Constrained Gradient Mining Significance constraint Csig: (cnt≥100) Probe constraint Cprb: (city=“Van”, cust_grp=“busi”, prod_grp=“*”) Gradient constraint Cgrad(cg, cp): (avg_price(cg)/avg_price(cp)≥1.3) Probe cell: satisfied Cprb Base cell Aggregated cell Siblings Ancestor 12/26/13 (c4, c2) satisfies Cgrad! Dimensions Measures cid Yr City Cst_grp Prd_grp Cnt Avg_price c1 00 Van Busi PC 300 2100 c2 * Van Busi PC 2800 1800 c3 * Tor Busi PC 7900 2350 c4 * * busi PC 58600 2250 Data Mining: Concepts and Techniques 69
- 70. Efficient Computing Cube-gradients Compute probe cells using Csig and Cprb The set of probe cells P is often very small Use probe P and constraints to find gradients Pushing selection deeply Set-oriented processing for probe cells Iceberg growing from low to high dimensionalities Dynamic pruning probe cells during growth Incorporating efficient iceberg cubing method 12/26/13 Data Mining: Concepts and Techniques 70
- 71. Chapter 4: Data Cube Computation and Data Generalization Efficient Computation of Data Cubes Exploration and Discovery in Multidimensional Databases Attribute-Oriented Induction ─ An Alternative Data Generalization Method 12/26/13 Data Mining: Concepts and Techniques 71
- 72. What is Concept Description? Descriptive vs. predictive data mining Descriptive mining: describes concepts or task-relevant data sets in concise, summarative, informative, discriminative forms Predictive mining: Based on data and analysis, constructs models for the database, and predicts the trend and properties of unknown data Concept description: Characterization: provides a concise and succinct summarization of the given collection of data Comparison: provides descriptions comparing two or more collections of data 12/26/13 Data Mining: Concepts and Techniques 72
- 73. Data Generalization and Summarization-based Characterization Data generalization A process which abstracts a large set of task-relevant data in a database from a low conceptual levels to higher ones. 1 2 3 4 5 Conceptual levels Approaches: 12/26/13 Data cube approach(OLAP approach) Attribute-oriented induction approach Data Mining: Concepts and Techniques 73
- 74. Concept Description vs. OLAP Similarity: Presentation of data summarization at multiple levels of abstraction. Data generalization Interactive drilling, pivoting, slicing and dicing. Differences: Can handle complex data types of the attributes and their aggregations Automated desired level allocation. Dimension relevance analysis and ranking when there are many relevant dimensions. Sophisticated typing on dimensions and measures. Analytical characterization: data dispersion analysis 12/26/13 Data Mining: Concepts and Techniques 74
- 75. Attribute-Oriented Induction Proposed in 1989 (KDD ‘89 workshop) Not confined to categorical data nor particular measures How it is done? Collect the task-relevant data (initial relation) using a relational database query Perform generalization by attribute removal or attribute generalization Apply aggregation by merging identical, generalized tuples and accumulating their respective counts Interactive presentation with users 12/26/13 Data Mining: Concepts and Techniques 75
- 76. Basic Principles of Attribute-Oriented Induction Data focusing: task-relevant data, including dimensions, and the result is the initial relation Attribute-removal: remove attribute A if there is a large set of distinct values for A but (1) there is no generalization operator on A, or (2) A’s higher level concepts are expressed in terms of other attributes Attribute-generalization: If there is a large set of distinct values for A, and there exists a set of generalization operators on A, then select an operator and generalize A Attribute-threshold control: typical 2-8, specified/default Generalized relation threshold control: control the final relation/rule size Data Mining: Concepts and Techniques 12/26/13 76
- 77. Attribute-Oriented Induction: Basic Algorithm InitialRel: Query processing of task-relevant data, deriving the initial relation. PreGen: Based on the analysis of the number of distinct values in each attribute, determine generalization plan for each attribute: removal? or how high to generalize? PrimeGen: Based on the PreGen plan, perform generalization to the right level to derive a “prime generalized relation”, accumulating the counts. Presentation: User interaction: (1) adjust levels by drilling, (2) pivoting, (3) mapping into rules, cross tabs, visualization presentations. 12/26/13 Data Mining: Concepts and Techniques 77
- 78. Example DMQL: Describe general characteristics of graduate students in the Big-University database use Big_University_DB mine characteristics as “Science_Students” in relevance to name, gender, major, birth_place, birth_date, residence, phone#, gpa from student where status in “graduate” Corresponding SQL statement: Select name, gender, major, birth_place, birth_date, residence, phone#, gpa from student where status in {“Msc”, “MBA”, “PhD” } 12/26/13 Data Mining: Concepts and Techniques 78
- 79. Class Characterization: An Example Name Gender Jim Initial Woodman Relation Scott Residence Phone # GPA Vancouver,BC, 8-12-76 Canada CS Montreal, Que, 28-7-75 Canada Physics Seattle, WA, USA 25-8-70 … … … M Major M F … Removed Retained 3511 Main St., Richmond 345 1st Ave., Richmond 687-4598 3.67 253-9106 3.70 125 Austin Ave., Burnaby … 420-5232 … 3.83 … Sci,Eng, Bus City Removed Excl, VG,.. Gender Major M F … Birth_date CS Lachance Laura Lee … Prime Generalized Relation Birth-Place Science Science … Country Age range Birth_region Age_range Residence GPA Canada Foreign … 20-25 25-30 … Richmond Burnaby … Very-good Excellent … Count 16 22 … Birth_Region Canada Foreign Total Gender M 14 30 F 10 22 32 Total 12/26/13 16 26 36 62 Data Mining: Concepts and Techniques 79
- 80. Presentation of Generalized Results Generalized relation: Relations where some or all attributes are generalized, with counts or other aggregation values accumulated. Cross tabulation: Mapping results into cross tabulation form (similar to contingency tables). Visualization techniques: Pie charts, bar charts, curves, cubes, and other visual forms. Quantitative characteristic rules: Mapping generalized result into characteristic rules with quantitative information associated with it, e.g., grad ( x) ∧ male( x) ⇒ birth _ region( x) ="Canada"[t :53%]∨ birth _ region( x) =" foreign"[t : 47%]. 12/26/13 Data Mining: Concepts and Techniques 80
- 81. Mining Class Comparisons Comparison: Comparing two or more classes Method: Partition the set of relevant data into the target class and the contrasting class(es) Generalize both classes to the same high level concepts Compare tuples with the same high level descriptions Present for every tuple its description and two measures support - distribution within single class comparison - distribution between classes Highlight the tuples with strong discriminant features Relevance Analysis: Find attributes (features) which best distinguish different classes 12/26/13 Data Mining: Concepts and Techniques 81
- 82. Quantitative Discriminant Rules Cj = target class qa = a generalized tuple covers some tuples of class but can also cover some tuples of contrasting class d-weight count(qa ∈Cj ) range: [0, 1] d − weight = m ∑count(q a ∈Ci ) i =1 quantitative discriminant rule form ∀ X, target_class(X) ⇐ condition(X) [d : d_weight] 12/26/13 Data Mining: Concepts and Techniques 82
- 83. Example: Quantitative Discriminant Rule Status Birth_country Age_range Gpa Count Graduate Canada 25-30 Good 90 Undergraduate Canada 25-30 Good 210 Count distribution between graduate and undergraduate students for a generalized tuple Quantitative discriminant rule ∀ X , graduate _ student ( X ) ⇐ birth _ country ( X ) =" Canada"∧ age _ range( X ) ="25 − 30"∧ gpa ( X ) =" good " [d : 30%] where 90/(90 + 210) = 30% 12/26/13 Data Mining: Concepts and Techniques 83
- 84. Class Description Quantitative characteristic rule ∀ X, target_class(X) ⇒ condition(X) [t : t_weight] necessary Quantitative discriminant rule ∀ X, target_class(X) ⇐ condition(X) [d : d_weight] sufficient Quantitative description rule ∀ X, target_class(X) ⇔ condition 1(X) [t : w1, d : w ′1] ∨ ... ∨ conditionn(X) [t : wn, d : w ′n] necessary and sufficient 12/26/13 Data Mining: Concepts and Techniques 84
- 85. Example: Quantitative Description Rule Location/item TV Computer Both_items Count t-wt d-wt Count t-wt d-wt Count t-wt d-wt Europe 80 25% 40% 240 75% 30% 320 100% 32% N_Am 120 17.65% 60% 560 82.35% 70% 680 100% 68% Both_ regions 200 20% 100% 800 80% 100% 1000 100% 100% Crosstab showing associated t-weight, d-weight values and total number (in thousands) of TVs and computers sold at AllElectronics in 1998 Quantitative description rule for target class Europe ∀ X, Europe(X) ⇔ (item(X) =" TV" ) [t : 25%, d : 40%] ∨ (item(X) =" computer" ) [t : 75%, d : 30%] 12/26/13 Data Mining: Concepts and Techniques 85
- 86. Summary Efficient algorithms for computing data cubes Multiway array aggregation BUC H-cubing Star-cubing High-D OLAP by minimal cubing Further development of data cube technology Discovery-drive cube Multi-feature cubes Cube-gradient analysis Anther generalization approach: Attribute-Oriented Induction 12/26/13 Data Mining: Concepts and Techniques 86
- 87. References (I) S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. VLDB’96 D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. SIGMOD’97 R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. ICDE’97 K. Beyer and R. Ramakrishnan. Bottom-Up Computation of Sparse and Iceberg CUBEs.. SIGMOD’99 Y. Chen, G. Dong, J. Han, B. W. Wah, and J. Wang, Multi-Dimensional Regression Analysis of Time-Series Data Streams, VLDB'02 G. Dong, J. Han, J. Lam, J. Pei, K. Wang. Mining Multi-dimensional Constrained Gradients in Data Cubes. VLDB’ 01 J. Han, Y. Cai and N. Cercone, Knowledge Discovery in Databases: An Attribute-Oriented Approach, VLDB'92 J. Han, J. Pei, G. Dong, K. Wang. Efficient Computation of Iceberg Cubes With Complex Measures. SIGMOD’01 12/26/13 Data Mining: Concepts and Techniques 87
- 88. References (II) L. V. S. Lakshmanan, J. Pei, and J. Han, Quotient Cube: How to Summarize the Semantics of a Data Cube, VLDB'02 X. Li, J. Han, and H. Gonzalez, High-Dimensional OLAP: A Minimal Cubing Approach, VLDB'04 K. Ross and D. Srivastava. Fast computation of sparse datacubes. VLDB’97 K. A. Ross, D. Srivastava, and D. Chatziantoniou. Complex aggregation at multiple granularities. EDBT'98 S. Sarawagi, R. Agrawal, and N. Megiddo. Discovery-driven exploration of OLAP data cubes. EDBT'98 G. Sathe and S. Sarawagi. Intelligent Rollups in Multidimensional OLAP Data. VLDB'01 D. Xin, J. Han, X. Li, B. W. Wah, Star-Cubing: Computing Iceberg Cubes by Top-Down and Bottom-Up Integration, VLDB'03 D. Xin, J. Han, Z. Shao, H. Liu, C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking, ICDE'06 W. Wang, H. Lu, J. Feng, J. X. Yu, Condensed Cube: An Effective Approach to Reducing Data Cube Size. ICDE’02 Y. Zhao, P. M. Deshpande, and J. F. Naughton. An array-based algorithm for simultaneous multidimensional aggregates. SIGMOD’97 12/26/13 Data Mining: Concepts and Techniques 88
- 89. 12/26/13 Data Mining: Concepts and Techniques 89
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