Machine Learning: Pattern Mining

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1 Machine Learning: Pattern Mining Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim Wintersemester 2007 / 2008

2 Pattern Mining Overview Itemsets Task Naive Algorithm Apriori Algorithm Data Structure Eclat Algorithm Association Rules Task Algorithm Summary

3 Overview Pattern Mining discovers regularities in data. Example: a transaction database of a supermarket: someone who buys chips also buys beer. Frequent patterns are found by counting the occurences in the data base. Types of patterns: itemsets, association rules, sequences,...

4 Example Shopping Carts Beer, Chips, Chocolate, Cookies Coke, Beer, Pizza, Chips Salad, Noodles, Tomatoes, Water Lasagne, Coke, Beer, Chips Oranges, Apple Juice, Rice, Cabbage, Sausage Diapers, Beer, Charcoal, Sausage Beer, Cabbage, Sausage, Chips...

5 Example Shopping Carts Beer, Chips, Chocolate, Cookies Coke, Beer, Pizza, Chips Salad, Noodles, Tomatoes, Water Lasagne, Coke, Beer, Chips Oranges, Apple Juice, Rice, Cabbage, Sausage Diapers, Beer, Charcoal, Sausage Beer, Cabbage, Sausage, Chips... Observations: Many customers buy beer. Beer and chips are often bought together. Customers who buy cabbage also buy sausage. Customers who buy something to eat also buy something to drink.

6 Outline Classification predicts class labels based on training data Clustering groups data based on similarity Pattern Mining discovers regularities in data

7 Itemsets Which itemsets frequently occur in the same transaction? Example: chips and beer are frequently bought together given Items I = {ii,..., i m } Data D P(I ) multiset Frequency threshold θ s to find Frequent sets L = {X P(I ) supportd (X ) θ s }

8 Definitions and Terms support D (X ) = {d D X d} D X is frequent / large iff support D (X ) θ s

9 Naive Algorithm function Naive(D, θ s ) L for all X P(I ) do if support D (X ) θ s then L L {X } end if end for return L end function

10 Example Data D,e a,,e a,b,d,e,d a, find itemsets with θ s 0.3 X frequent # D (X ) > 2

11 Example Data D,e a,,e a,b,d,e,d a, P(I ) # a? b? c? d? e? a,b?? a,d? a,e?? b,d? b,e? c,d? c,e? P(I ) # d,e? a,? a,b,d??,d?,e? c,d,e? a,,d? a,,e? a,b,d,e?,d,e?,d,e? a,,d,e?

12 Example Data D,e a,,e a,b,d,e,d a, P(I ) # a 7 b 7 c 6 d 2 e 5 a,b 5 4 a,d 1 a,e 5 4 b,d 2 b,e 4 c,d 1 c,e 2 P(I ) # d,e 1 a, 2 a,b,d 1 4,d 1,e 1 c,d,e 0 a,,d 0 a,,e 1 a,b,d,e 1,d,e 0,d,e 0 a,,d,e 0

13 Example Data D,e a,,e a,b,d,e,d a, P(I ) # a 7 b 7 c 6 d 2 e 5 a,b 5 4 a,d 1 a,e 5 4 b,d 2 b,e 4 c,d 1 c,e 2 P(I ) # d,e 1 a, 2 a,b,d 1 4,d 1,e 1 c,d,e 0 a,,d 0 a,,e 1 a,b,d,e 1,d,e 0,d,e 0 a,,d,e 0 L = {{a}, {b}, {c}, {e}, {a, b}, {a, c}, {a, e}, {b, c}, {b, e}, {a, b, e}}

14 Properties of Naive Algorithm returns correct result always terminates But: counting support for each itemset X P(I ) is not applicable as P(I ) is exponential in I

15 Observations support D (X ) support D (X Y ) support D (X ) θ s Y : Y X : support D (Y ) θ s all subsets of a frequent set are frequent support D (X ) < θ s Y : Y X : support D (Y ) < θ s all supersets of an infrequent set X are not frequent example: support D ({a, b}) support D ({a, b, c, d})

16 Apriori Algorithm Breadth-first/ levelwise search 1. find frequent itemsets of length 1 2. find frequent itemsets of length only explores itemsets where all subsets are known to be frequent

17 Apriori Algorithm function Apriori(D, θ s ) k 1 L k {{i} i I, support D ({i}) θ s } while L k do C k+1 generatecandidates(l k, k + 1) L k+1 {X C k+1 support D (X ) θ s } k k + 1 end while return k=1 end function L k

18 Candidate Generation generates candidates of length k from frequent itemsets L of length k 1 function generatecandidates(l, k) C {X Y X, Y L X Y = k} C {X C Y X : Y = k 1 Y L} return C end function

19 Example Data D,e a,,e a,b,d,e,d a, find itemsets with θ s 0.3 X frequent # D (X ) > 2

20 Example Data D,e a,,e a,b,d,e,d a, C 1 # a? b? c? d? e?

21 Example Data D,e a,,e a,b,d,e,d a, C 1 # a 7 b 7 c 6 d 2 e 5

22 Example Data D,e a,,e a,b,d,e,d a, C 1 # a 7 b 7 c 6 d 2 e 5

23 Example Data D,e a,,e a,b,d,e,d a, C 1 # a 7 b 7 c 6 d 2 e 5 L 1 a b c e

24 Example Data D,e a,,e a,b,d,e,d a, C 2 # a,b?? a,e?? b,e? c,e? L 1 a b c e

25 Example Data D,e a,,e a,b,d,e,d a, C 2 # a,b 5 4 a,e 5 4 b,e 4 c,e 2

26 Example Data D,e a,,e a,b,d,e,d a, C 2 # a,b 5 4 a,e 5 4 b,e 4 c,e 2

27 Example Data D,e a,,e a,b,d,e,d a, C 2 # a,b 5 4 a,e 5 4 b,e 4 c,e 2 L 2 a,b a,e b,e

28 Example Data D,e a,,e a,b,d,e,d a, C 3 # a,??,e?,e? L 2 a,b a,e b,e

29 Example Data D,e a,,e a,b,d,e,d a, C 3 # a,?? Pruning: {c, e} L 2

30 Example Data D,e a,,e a,b,d,e,d a, C 3 # a, 2 4

31 Example Data D,e a,,e a,b,d,e,d a, C 3 # a, 2 4

32 Example Data D,e a,,e a,b,d,e,d a, C 3 # a, 2 4 L 3

33 Example Data D,e a,,e a,b,d,e,d a, C 4 # - - L 3

34 Example Data D,e a,,e a,b,d,e,d a, L 1 a b c e L 2 a,b a,e b,e L 3 L = {{a}, {b}, {c}, {e}, {a, b}, {a, c}, {a, e}, {b, c}, {b, e}, {a, b, e}}

35 Trie / Prefix Tree For candidate generation and frequency counting, a trie can be used: a trie is a tree each node contains an item and a frequency counter each path from the root to a node corresponses to an itemset the k-th level represents itemsets of length k the items in a trie are ordered

36 Example Prefix Tree Data D,e a,,e a,b,d,e,d a, Ø _ a 7 b 7 c 6 e 5 b 5 c 4 e 5 c 4 e 4 e 4

37 Example #{} = 4 Data D,e a,,e a,b,d,e,d a, Ø _ a 7 b 7 c 6 e 5 b 5 c 4 e 5 c 4 e 4 e 4

38 Example Data D,e a,,e a,b,d,e,d a, #{b} = 7 Ø _ a 7 b 7 c 6 e 5 b 5 c 4 e 5 c 4 e 4 e 4

39 Example Data D,e a,,e a,b,d,e,d a, #{b,e} = 4 Ø _ a 7 b 7 c 6 e 5 b 5 c 4 e 5 c 4 e 4 e 4

40 Trie: Frequency Counting To count frequencies with a trie, each transaction d D is handled the following way: 1. sort d 2. start at the root 3. for each item i d follow the node i, increase it by one and recursively repeat this for d \ {i}

41 Trie: Candidate Generation Candidates of length k can be generated from a trie of depth k 1: 1. for each node at level k 1 append its siblings 2. prune infrequent childs

42 Trie: Example Data D,e a,,e a,b,d,e,d a, find itemsets with θ s 0.3 X frequent # D (X ) > 2... see blackboard...

43 Eclat Algorithm Algorithm for itemset mining Depth-first algorithm Vertical data base layout For each pattern: store the cover, i.e. all transactions that include this pattern. e.g. (a, {d 1, d 3, d 4, d 5, d 7, d 8, d 9 }) Count frequency by intersection

44 Eclat Algorithm function Eclat(D, θ s ) C = {{(i, {d D i d}) i I L = (i, D i ) C D } i D θ s return EclatRecursion(L,, θ s ) end function

45 Eclat Algorithm function EclatRecursion(L, p, θ s ) F for all (i, D i ) L do q p {i} F F {p} C q { {(j, D i D j ) (j, D j ) L, j > i} L q (k, D k ) C q D } k θ s D if L q then F F EclatRecursion(L q, q, θ s } end if end for return F end function

46 Example Data D,e a,,e a,b,d,e,d a, find itemsets with θ s 0.3 X frequent # D (X ) > 2... see blackboard...

47 Association Rules Which itemssets Y occur often if another itemset X appears? X Y Example: a customer buying diapers also buys beer {diapers} {beer} given Items I = {ii,..., i m } Data D P(I ) multiset Frequency thresholds θs Confidence threshold θ c to find Rules R = {X Y supportd (X Y ) θ s confidence D (X Y ) θ c }

48 Definitions and Terms support measures how often the rule X Y appears support D (X Y ) = support D (X Y ) X Y is frequent / large iff support D (X Y ) θ s confidence measures how likely it is that Y appears if X is present. confidenced (X Y ) = support D(X Y ) support D (X ) for a rule X Y, Y is called head and X is called body

49 Example Data D,e a,,e a,b,d,e,d a, find rules with θ s 0.3 X frequent # D (X ) > 2 find rules with θ c 0.8

50 Example Data D,e a,,e a,b,d,e,d a, confidence D (X Y ) = support D(X Y ) support D (X ) support D (X Y ) = support D (X Y ) L = {{a}, {b}, {c}, {e}, {a, b}, {a, c}, {a, e}, {b, c}, {b, e}, {a, b, e}}... see blackboard...

51 Example Data D,e a,,e a,b,d,e,d a, R = {e a, e b, e ab, ab e, ae b, be a} {X X L}

52 Observations expanding the head of a rule by an item of the body, results in a rule with less or equal confidence. proof: confidence D (X \ Z Y Z) confidence D (X Y ) confidence D (X \ Z Y Z) = support D((X \ Z) (Y Z)) support D (X \ Z) support D(X Y ) support D (X ) = confidence D (X Y ) = support D(X Y Z) support D (X \ Z) example: confidence D ({a, b} {c, d}) confidence D ({a, b, c} {d})

53 Algorithm Association rule mining is done in two steps: 1. find frequent itemsets (see itemset mining) 2. extract rules from the frequent itemsets

54 AssociationRules Algorithm function AssociationRules(D, θ s, θ c ) L Apriori(D, θ s ) R for all I L do k 1 C k {{i} i I } while C k do H k {X C k confidence D (I \ X X ) θ c } C k+1 generatecandidateheads(h k, k + 1) k k + 1 end while R R {I \ X X X H k } {I } end for return R end function k=1

55 Candidate Generation for Heads of Rules generates candidate heads of length k from heads H of length k 1 function generatecandidateheads(h, k) C {X Y X, Y H X Y = k} C {X C Y X : Y = k 1 Y H} return C end function

56 Remarks Calculating the confidence can be reduced to calculating the support: confidence D (I \ X X ) = support D((I \ X ) X ) support D (I \ X ) support D(I ) support D (I \ X ) θ c θ c support D (I \ X ) 1 θ c support D (I ) If θ c θ s, the values for support D can be looked up in the trie and no database pass is necessary.

57 Example Trace of inner loop of the algorithm AssociationRules for I = {a, b, e}.... see blackboard...

58 Outlook Extensions to Apriori and Eclat Further pattern: sequences, trees,... Background knowledge: e.g. taxonomies

59 Sequence mining Takes the time into account, when an action is performed. E.g. A database of courses attended by a student in one term, i.e. sequences of sets: Student1: ({linear algebra, c++, algorithm theory}, {machine learning, numerics, economics}, {bayessian networks}) Student2: ({linear algebra, java}, {software engineering}, {numerics}) Student3: ({linear algebra, java, algorithm theory}, {economics}, {machine learning, numerics}, {bayessian networks})... A frequent sequence might be ({linear algebra, algorithm theory}, {machine learning, numerics}, {bayessian networks})

60 Use taxonomies Background knowledge in terms of taxonomies might be used for mining patterns. E.g. The following taxonomy is given over subjects linear algebra isa mathematics mathematics isa science computer science isa science In the student database one could mine the association rule using the taxonomy: if someone has attended machine learning then (s)he also has attended some mathematic lecture {machine learning} {mathematics}

61 Conclusion Task: Finding frequent patterns in database. Efficient algorithms explore only promising candidates by pruning. Mining association rules can be reduced to mining itemsets with an additional post processing step.

62 Literature R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pages Morgan Kaufmann, R. Agrawal and R. Srikant. Mining sequential patterns. In P. S. Yu and A. S. P. Chen, editors, Eleventh International Conference on Data Engineering, pages 3 14, Taipei, Taiwan, IEEE Computer Society Press. L. Schmidt-Thieme. Algorithmic features of eclat

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