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Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.

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- 1. Prof. Pier Luca Lanzi Association Rules Data Mining andText Mining (UIC 583 @ Politecnico di Milano)
- 2. Prof. Pier Luca Lanzi Readings • “Data Mining and Analysis” by Zaki & Meira §Chapter 8 §Section 9.1 §Chapter 10 up to Section 10.2.1 included §Section 12.1 • http://www.dataminingbook.info 2
- 3. Prof. Pier Luca Lanzi
- 4. Prof. Pier Luca Lanzi Market-Basket Transactions Bread Peanuts Milk Fruit Jam Bread Jam Soda Chips Milk Fruit Steak Jam Soda Chips Bread Jam Soda Chips Milk Bread Fruit Soda Chips Milk Jam Soda Peanuts Milk Fruit Fruit Soda Peanuts Milk Fruit Peanuts Cheese Yogurt 4
- 5. Prof. Pier Luca Lanzi Association Rules 5
- 6. Prof. Pier Luca Lanzi What is Association Rule Mining? • Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories • Applications §Basket data analysis §Cross-marketing §Catalog design §… 6
- 7. Prof. Pier Luca Lanzi What is Association Rule Mining? 7 • Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction • Examples §{bread} Þ {milk} §{soda} Þ {chips} §{bread} Þ {jam} TID Items 1 Bread, Peanuts, Milk, Fruit, Jam 2 Bread, Jam, Soda, Chips, Milk, Fruit 3 Steak, Jam, Soda, Chips, Bread 4 Jam, Soda, Peanuts, Milk, Fruit 5 Jam, Soda, Chips, Milk, Bread 6 Fruit, Soda, Chips, Milk 7 Fruit, Soda, Peanuts, Milk 8 Fruit, Peanuts, Cheese, Yogurt
- 8. Prof. Pier Luca Lanzi Frequent Itemset • Itemset § A collection of one or more items, e.g., {milk, bread, jam} § k-itemset, an itemset that contains k items • Support count (s) § Frequency of occurrence of an itemset § s({Milk, Bread}) = 3 s({Soda, Chips}) = 4 • Support § Fraction of transactions that contain an itemset § s({Milk, Bread}) = 3/8 s({Soda, Chips}) = 4/8 • Frequent Itemset § An itemset whose support is greater than or equal to a minsup threshold 8 TID Items 1 Bread, Peanuts, Milk, Fruit, Jam 2 Bread, Jam, Soda, Chips, Milk, Fruit 3 Steak, Jam, Soda, Chips, Bread 4 Jam, Soda, Peanuts, Milk, Fruit 5 Jam, Soda, Chips, Milk, Bread 6 Fruit, Soda, Chips, Milk 7 Fruit, Soda, Peanuts, Milk 8 Fruit, Peanuts, Cheese, Yogurt
- 9. Prof. Pier Luca Lanzi What is An Association Rule? • Implication of the form X Þ Y, where X and Y are itemsets • Example, {bread} Þ {milk} • Rule Evaluation Metrics, Suppor & Confidence • Support (s) §Fraction of transactions that contain both X and Y • Confidence (c) §Measures how often items in Y appear in transactions that contain X 9
- 10. Prof. Pier Luca Lanzi What is the Goal? • Given a set of transactions T, the goal of association rule mining is to find all rules having §support ≥ minsup threshold §confidence ≥ minconf threshold • Brute-force approach §List all possible association rules §Compute the support and confidence for each rule §Prune rules that fail the minsup and minconf thresholds • Brute-force approach is computationally prohibitive! 10
- 11. Prof. Pier Luca Lanzi Mining Association Rules • {Bread, Jam} Þ {Milk} s=0.4 c=0.75 • {Milk, Jam} Þ {Bread} s=0.4 c=0.75 • {Bread} Þ {Milk, Jam} s=0.4 c=0.75 • {Jam} Þ {Bread, Milk} s=0.4 c=0.6 • {Milk} Þ {Bread, Jam} s=0.4 c=0.5 • All the above rules are binary partitions of the same itemset {Milk, Bread, Jam} • Rules originating from the same itemset have identical support but can have different confidence • We can decouple the support and confidence requirements! 11
- 12. Prof. Pier Luca Lanzi Mining Association Rules 12
- 13. Prof. Pier Luca Lanzi Mining Association Rules: Two Step Approach • Frequent Itemset Generation §Generate all itemsets whose support ³ minsup • Rule Generation §Generate high confidence rules from frequent itemset §Each rule is a binary partitioning of a frequent itemset • Frequent itemset generation is computationally expensive 13
- 14. Prof. Pier Luca Lanzi Frequent Itemset Generation null AB AC AD AE BC BD BE CD CE DE A B C D E ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE ABCD ABCE ABDE ACDE BCDE ABCDE Given d items, there are 2d possible candidate itemsets 14
- 15. Prof. Pier Luca Lanzi Brute Force 15
- 16. Prof. Pier Luca Lanzi Frequent Itemset Generation • Brute-force approach: §Each itemset in the lattice is a candidate frequent itemset §Count the support of each candidate by scanning the database §Match each transaction against every candidate §Complexity ~ O(NMw) => Expensive since M = 2d TID Items 1 Bread, Peanuts, Milk, Fruit, Jam 2 Bread, Jam, Soda, Chips, Milk, Fruit 3 Steak, Jam, Soda, Chips, Bread 4 Jam, Soda, Peanuts, Milk, Fruit 5 Jam, Soda, Chips, Milk, Bread 6 Fruit, Soda, Chips, Milk 7 Fruit, Soda, Peanuts, Milk 8 Fruit, Peanuts, Cheese, Yogurt w N M candidates 16
- 17. Prof. Pier Luca Lanzi Computational Complexity • Given d unique items there are 2d possible itemsets • Total number of possible association rules: • For d=6, there are 602 rules 17
- 18. Prof. Pier Luca Lanzi Frequent Itemset Generation Strategies • Reduce the number of candidates (M) §Complete search has M=2d §Use pruning techniques to reduce M • Reduce the number of transactions (N) §Reduce size of N as the size of itemset increases • Reduce the number of comparisons (NM) §Use efficient data structures to store the candidates or transactions §No need to match every candidate against every transaction 18
- 19. Prof. Pier Luca Lanzi Reducing the Number of Candidates • Apriori principle §If an itemset is frequent, then all of its subsets must also be frequent • Apriori principle holds due to the following property of the support measure: • Support of an itemset never exceeds the support of its subsets • This is known as the anti-monotone property of support 19
- 20. Prof. Pier Luca Lanzi Illustrating Apriori Principle Found to be Infrequent null AB AC AD AE BC BD BE CD CE DE A B C D E ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE ABCD ABCE ABDE ACDE BCDE ABCDE null AB AC AD AE BC BD BE CD CE DE A B C D E ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE ABCD ABCE ABDE ACDE BCDE ABCDE Pruned supersets 20
- 21. Prof. Pier Luca Lanzi How does the Apriori principle work? Item Count Bread Peanuts Milk Fruit Jam Soda Chips Steak Cheese Yogurt 4 4 6 6 5 6 4 1 1 1 2-Itemset Count Bread, Jam Peanuts, Fruit Milk, Fruit Milk, Jam Milk, Soda Fruit, Soda Jam, Soda Soda, Chips 4 4 5 4 5 4 4 4 3-Itemset Count Milk, Fruit, Soda 4 1-itemsets Minimum Support = 4 2-itemsets 3-itemsets 21
- 22. Prof. Pier Luca Lanzi Example • Given the following database and a min support of 3, generate all the frequent itemsets 22
- 23. Prof. Pier Luca Lanzi 23
- 24. Prof. Pier Luca Lanzi The Apriori Algorithm • Let k=1 • Generate frequent itemsets of length 1 • Repeat until no new frequent itemsets are identified §Generate length (k+1) candidate itemsets from length k frequent itemsets §Prune candidate itemsets containing subsets of length k that are infrequent §Count the support of each candidate by scanning the DB §Eliminate candidates that are infrequent, leaving only those that are frequent 24
- 25. Prof. Pier Luca Lanzi Apriori Algorithm 25
- 26. Prof. Pier Luca Lanzi Apriori Algorithm 26
- 27. Prof. Pier Luca Lanzi Apriori Algorithm Example 27
- 28. Prof. Pier Luca Lanzi Eclat Algorithm 28
- 29. Prof. Pier Luca Lanzi The Eclat Algorithm • Leverages the tidsets directly for support computation. • The support of a candidate itemset can be computed by intersecting the tidsets of suitably chosen subsets. • Given t(X) and t(Y) for any two frequent itemsets X and Y, then t(XY)=t(X) ∧ t(Y) • And sup(XY) = |t(XY)| 29
- 30. Prof. Pier Luca Lanzi Example 30
- 31. Prof. Pier Luca Lanzi 31
- 32. Prof. Pier Luca Lanzi Frequent Patterns Mining Without Candidate Generation
- 33. Prof. Pier Luca Lanzi Apriori’s Performance Bottlenecks • The core of the Apriori algorithm §Use frequent (k–1)-itemsets to generate candidate frequent k-itemsets §Use database scan and pattern matching to collect counts for the candidate itemsets • Huge candidate sets: §104 frequent 1-itemset will generate 107 candidate 2-itemsets §To discover a frequent pattern of size 100, e.g., {a1, a2, …, a100}, needs to generate 2100 ~1030 candidates. §Multiple scans of database, it needs (n+1) scans, n is the length of the longest pattern 33
- 34. Prof. Pier Luca Lanzi Mining Frequent Patterns Without Candidate Generation • Compress a large database into a compact, Frequent-Pattern tree (FP-tree) structure §Highly condensed, but complete for frequent pattern mining §Avoid costly database scans • Use an efficient, FP-tree-based frequent pattern mining method • A divide-and-conquer methodology: decompose mining tasks into smaller ones • Avoid candidate generation: sub-database test only 34
- 35. Prof. Pier Luca Lanzi The FP-Growth Algorithm • Leave the generate-and-test paradigm of Apriori • Data sets are encoded using a compact structure, the FP-tree • Frequent itemsets are extracted directly from the FP-tree • Major Steps to mine FP-tree §Construct conditional pattern base for each node in the FP-tree §Construct conditional FP-tree from each conditional pattern-base §Recursively mine conditional FP-trees and grow frequent patterns obtained so far §If the conditional FP-tree contains a single path, simply enumerate all the patterns 35
- 36. Prof. Pier Luca Lanzi 36
- 37. Prof. Pier Luca Lanzi
- 38. Prof. Pier Luca Lanzi
- 39. Prof. Pier Luca Lanzi Rule Generation
- 40. Prof. Pier Luca Lanzi Rule Generation • Given a frequent itemset L, find all non-empty subsets f ⊂ L such that f → L – f satisfies the minimum confidence requirement • If {A,B,C,D} is a frequent itemset, candidate rules: ABC → D, ABD → C, ACD → B, BCD → A, A → BCD, B → ACD, C → ABD, D → ABC, AB → CD, AC → BD, AD → BC, BC → AD, BD → AC, CD → AB • If |L| = k, then there are 2k – 2 candidate association rules (ignoring L → ⦰ and ⦰ → L) 40
- 41. Prof. Pier Luca Lanzi How to efficiently generate rules from frequent itemsets? • Confidence does not have an anti-monotone property • c(ABC →D) can be larger or smaller than c(AB →D) • But confidence of rules generated from the same itemset has an anti-monotone property • L = {A,B,C,D}: c(ABC → D) ≥ c(AB → CD) ≥ c(A → BCD) • Confidence is anti-monotone with respect to the number of items on the right hand side of the rule 41
- 42. Prof. Pier Luca Lanzi Rule Generation for Apriori Algorithm ABCD=>{ } BCD=>A ACD=>B ABD=>C ABC=>D BC=>ADBD=>ACCD=>AB AD=>BC AC=>BD AB=>CD D=>ABC C=>ABD B=>ACD A=>BCD ABCD=>{ } BCD=>A ACD=>B ABD=>C ABC=>D BC=>ADBD=>ACCD=>AB AD=>BC AC=>BD AB=>CD D=>ABC C=>ABD B=>ACD A=>BCD Pruned Rules Low Confidence Rule 42
- 43. Prof. Pier Luca Lanzi Rule Generation for Apriori Algorithm • Candidate rule is generated by merging two rules that share the same prefix in the rule consequent • join(CD=>AB,BD=>AC) would produce the candidate rule D => ABC • Prune rule D=>ABC if its subset AD=>BC does not have high confidence BD=>ACCD=>AB D=>ABC 43
- 44. Prof. Pier Luca Lanzi 44
- 45. Prof. Pier Luca Lanzi Effect of Support Distribution • Many real data sets have skewed support distribution Support distribution of a retail data set 45
- 46. Prof. Pier Luca Lanzi How to Set an Appropriate Minsup? • If minsup is set too high, we could miss itemsets involving interesting rare items (e.g., expensive products) • If minsup is set too low, it is computationally expensive and the number of itemsets is very large • A single minimum support threshold may not be effective 46
- 47. Prof. Pier Luca Lanzi Association Rule Mining in R library(arulesViz) library(arules) data("Adult") rules <- apriori(Adult, parameter = list(supp = 0.8, conf = 0.9,target = "rules")) summary(rules) inspect(rules) plot(rules) plot(rules, method="graph", control=list(type="items")) 47
- 48. Prof. Pier Luca Lanzi FP-growth vs. Apriori: Scalability with the Support Threshold 0 10 20 30 40 50 60 70 80 90 100 0 0.5 1 1.5 2 2.5 3 Support threshold(%) Runtime(sec.) D1 FP-grow th runtime D1 Apriori runtime 48
- 49. Prof. Pier Luca Lanzi Benefits of the FP-Tree Structure • Completeness §Preserve complete information for frequent pattern mining §Never break a long pattern of any transaction • Compactness §Reduce irrelevant info—infrequent items are gone §Items in frequency descending order: the more frequently occurring, the more likely to be shared §Never be larger than the original database (not count node- links and the count field) §For Connect-4 DB, compression ratio could be over 100 49
- 50. Prof. Pier Luca Lanzi Why is FP-Growth the Winner? • Divide-and-conquer: §decompose both the mining task and DB according to the frequent patterns obtained so far §leads to focused search of smaller databases • Other factors §No candidate generation, no candidate test §Compressed database: FP-tree structure §No repeated scan of entire database §Basic ops—counting local freq items and building sub FP-tree, no pattern search and matching 50
- 51. Prof. Pier Luca Lanzi Rule Assessment Measures
- 52. Prof. Pier Luca Lanzi Lift • Lift is the ratio of the observed joint probability of X and Y to the expected joint probability if they were statistically independent, lift(X → Y) = sup(XUY)/sup(X)sup(Y) = conf(X → Y)/sup(Y) • Lift is a measure of the deviation from stochastic independence (if it is 1 then X and Y are independent) • Lift also measure the surprise of the rule. A lift close to 1means that the support of a rule is expected considering the supports of its components. • We typically look for values of lift that are much larger (i.e., above expectation) or much smaller (i.e., below expectation) than one. 52
- 53. Prof. Pier Luca Lanzi Leverage • Leverage is computed as the difference between the observed and expected joint probability of XY assuming that X and Y are independent leverage(X → Y) = sup(XUY) - sup(X)sup(Y) 53
- 54. Prof. Pier Luca Lanzi Summarizing Itemsets
- 55. Prof. Pier Luca Lanzi Maximal Frequent Itemsets • A frequent itemset X is called maximal if it has no frequent supersets • The set of all maximal frequent itemsets, given as M = {X | X ∈ F and !∃Y ⊃ X, such that Y ∈ F } • M is a condensed representation of the set of all frequent itemset F, because we can determine whether any itemset is frequent or not using M • If there is a maximal itemset Z such that X ⊆ Z, then X must be frequent, otherwise X cannot be frequent • However, M alone cannot be used to determine sup(X), we can only use to have a lower-bound, that is, sup(X) ≥ sup(Z) if X ⊆ Z ∈M. 55
- 56. Prof. Pier Luca Lanzi Closed Frequent Itemsets • An itemset X is closed if all supersets of X have strictly less support, that is, sup(X) > sup(Y), for all Y ⊃ X • The set of all closed frequent itemsets C is a condensed representation, as we can determine whether an itemset X is frequent, as well as the exact support of X using C alone 56
- 57. Prof. Pier Luca Lanzi Minimal Generators • A frequent itemset X is a minimal generator if it has no subsets with the same support G = { X | X ∈ F and !∃Y ⊂ X, such that sup(X) = sup(Y) } • Thus, all subsets of X have strictly higher support, that is, sup(X) < sup(Y) 57
- 58. Prof. Pier Luca Lanzi 58
- 59. Prof. Pier Luca Lanzi Shaded boxes are closed itemsets Simple boxes are generators Shaded boxes with double lines are maximal itemsets
- 60. Prof. Pier Luca Lanzi Mining Frequent Sequences
- 61. Prof. Pier Luca Lanzi Mining Frequent Sequences
- 62. Prof. Pier Luca Lanzi Sequential Pattern Mining • Association rule mining does not consider the order of transactions • In many applications such orderings are significant • In market basket analysis, it is interesting to know whether people buy some items in sequence • Example: buying bed first and then bed sheets later • In Web usage mining, it is useful to find navigational patterns of users in a Web site from sequences of page visits of users 62
- 63. Prof. Pier Luca Lanzi Examples of Sequence • Web sequence §< {Homepage} {Electronics} {Digital Cameras} {Canon Digital Camera} {Shopping Cart} {Order Confirmation} {Return to Shopping} > • Sequence of initiating events causing the accident at 3-mile Island: §<{clogged resin} {outlet valve closure} {loss of feedwater} {condenser polisher outlet valve shut} {booster pumps trip} {main waterpump trips} {main turbine trips} {reactor pressure increases}> • Sequence of books checked out at a library: §<{Fellowship of the Ring} {The Two Towers} {Return of the King}> 63
- 64. Prof. Pier Luca Lanzi Basic Concepts • Let I = {i1, i2, …, im} be a set of items. • A sequence is an ordered list of itemsets. • We denote a sequence s by <a1, a2, …, ar> • An element (or itemset of a sequence is denoted by {x1, x2, …, xk} where xi is a subset of {i1, i2, …, im} • We assume without loss of generality that items in an element of a sequence are in lexicographic order 64
- 65. Prof. Pier Luca Lanzi Basic concepts • The size of a sequence is the number of elements (or itemsets) in the sequence • The length of a sequence is the number of items in the sequence (a sequence of length k is called k-sequence) • Given two sequence s1 = <a1, a2, …, ar> and s2 = <b1, b2, …, bv> we say that s1 is a subsequnce of s2 §If there exists a set of integers 1 ≤ j1 < j2 < … < jr-1 < jr ≤ v such that for all i, ai ⊂bji • 65
- 66. Prof. Pier Luca Lanzi Frequent Sequences • Given a database D {s1, …, sN} of N sequences and a sequence r, we define the support count of r as sup(r) = | {si | r is a subsequence of si}| • And the support (or relative support), rsup(r) = sup(r)/N 66
- 67. Prof. Pier Luca Lanzi Example • Let I = {1, 2, 3, 4, 5, 6, 7, 8, 9}. • The sequence {3}{4, 5}{8} is contained in • (or is a subsequence of) {6} {3, 7}{9}{4, 5, 8}{3, 8} • In fact, {3} {3, 7}, {4, 5} {4, 5, 8}, and {8} {3, 8} • However, {3}{8} is not contained in {3, 8} or vice versa • The size of the sequence {3}{4, 5}{8} is 3, and the length of the sequence is 4. 67
- 68. Prof. Pier Luca Lanzi Goal of Sequential Pattern Mining • The input is a set S of input data sequences (or sequence database) • The problem of mining sequential patterns is to find all the sequences that have a user-specified minimum support • Each such sequence is called a frequent sequence, or a sequential pattern • The support for a sequence is the fraction of total data sequences in S that contains this sequence 68
- 69. Prof. Pier Luca Lanzi Terminology • Itemset §Non-empty set of items §Each itemset is mapped to an integer • Sequence §Ordered list of itemsets • Support for a Sequence §Fraction of total customers that support a sequence. • Maximal Sequence §A sequence that is not contained in any other sequence • Large Sequence §Sequence that meets minisup 69
- 70. Prof. Pier Luca Lanzi Example • Given the following sequence database • With a minsup of 3, the set of frequent subsequences is §A(3), G(3), T(3) §AA(3), AG(3), GA(3), GG(3) §AAG(3), GAA(3), GAG(3) §GAAG(3) 70
- 71. Prof. Pier Luca Lanzi The GSP Algorithm 71
- 72. Prof. Pier Luca Lanzi The GSP Algorithm 72
- 73. Prof. Pier Luca Lanzi The GSP Algorithm 73
- 74. Prof. Pier Luca Lanzi
- 75. Prof. Pier Luca Lanzi Example Customer ID Transaction Time Items Bought 1 June 25 '93 30 1 June 30 '93 90 2 June 10 '93 10,20 2 June 15 '93 30 2 June 20 '93 40,60,70 3 June 25 '93 30,50,70 4 June 25 '93 30 4 June 30 '93 40,70 4 July 25 '93 90 5 June 12 '93 90 Customer ID Customer Sequence 1 < (30) (90) > 2 < (10 20) (30) (40 60 70) > 3 < (30) (50) (70) > 4 < (30) (40 70) (90) > 5 < (90) > Maximal seq with support > 40% < (30) (90) > < (30) (40 70) > Note: Use Minisup of 40%, no less than two customers must support the sequence < (10 20) (30) > Does not have enough support (Only by Customer #2) < (30) >, < (70) >, < (30) (40) > … are not maximal. 75
- 76. Prof. Pier Luca Lanzi Association Rules for Classification 76
- 77. Prof. Pier Luca Lanzi Mining Association Rules for Classification (the CBA algorithm) • Association rule mining assumes that the data consist of a set of transactions. Thus, the typical tabular representation of data used in classification must be mapped into such a format. • Association rule mining is then applied to the new dataset and the search is focused on association rules in which the tail identifies a class label X → ci (where ci is a class label) • The association rules are pruned using the pessimistic error-based method used in C4.5 • Finally, rules are sorted to build the final classifier. 77
- 78. Prof. Pier Luca Lanzi JRip Model (humidity = high) and (outlook = sunny) => play=no (3.0/0.0) (outlook = rainy) and (windy = TRUE) => play=no (2.0/0.0) => play=yes (9.0/0.0) 78
- 79. Prof. Pier Luca Lanzi One Rule Model outlook: overcast -> yes rainy-> yes sunny-> no (10/14 instances correct) 79
- 80. Prof. Pier Luca Lanzi CBA Model outlook=overcast ==> play=yes humidity=normal windy=FALSE ==> play=yes outlook=rainy windy=FALSE ==> play=yes outlook=sunny humidity=high ==> play=no outlook=rainy windy=TRUE ==> play=no (default class is the majority class) 80
- 81. Prof. Pier Luca Lanzi Run the KNIME workflow and the Python notebooks for this lecture

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