association rule mining. mining association rules in large databases association rule mining ...

Association Rule Mining

Mining Association Rules in Large Databases

Association rule mining

Algorithms Apriori and FP-Growth

Max and closed patterns

Mining various kinds of association/correlation

rules

Max-patterns & Close-patterns If there are frequent patterns with many

items, enumerating all of them is costly. We may be interested in finding the

‘boundary’ frequent patterns. Two types…

Max-patterns Frequent pattern {a1, …, a100} (100

1) + (100

2) + … + (11

00

00) = 2100-1 = 1.27*1030

frequent sub-patterns! Max-pattern: frequent patterns without

proper frequent super pattern BCDE, ACD are max-patterns BCD is not a max-pattern

Tid Items

10 A,B,C,D,E

20 B,C,D,E,

30 A,C,D,FMin_sup=2

MaxMiner: Mining Max-patterns

Idea: generate the complete set-enumeration tree one level at a time, while prune if applicable.

(ABCD)

A (BCD) B (CD) C (D) D ()

AB (CD) AC (D) AD () BC (D) BD () CD ()

ABC (C)

ABCD ()

ABD () ACD () BCD ()

Local Pruning Techniques (e.g. at node A)

Check the frequency of ABCD and AB, AC, AD. If ABCD is frequent, prune the whole sub-tree. If AC is NOT frequent, remove C from the

parenthesis before expanding.

(ABCD)

A (BCD) B (CD) C (D) D ()

AB (CD) AC (D) AD () BC (D) BD () CD ()

ABC (C)

ABCD ()

ABD () ACD () BCD ()

Algorithm MaxMiner

Initially, generate one node N= , where h(N)= and t(N)={A,B,C,D}.

Consider expanding N, If h(N)t(N) is frequent, do not expand N. If for some it(N), h(N){i} is NOT frequent,

remove i from t(N) before expanding N. Apply global pruning techniques…

(ABCD)

Global Pruning Technique (across sub-trees)

When a max pattern is identified (e.g. ABCD), prune all nodes (e.g. B, C and D) where h(N)t(N) is a sub-set of it (e.g. ABCD).

(ABCD)

A (BCD) B (CD) C (D) D ()

AB (CD) AC (D) AD () BC (D) BD () CD ()

ABC (C)

ABCD ()

ABD () ACD () BCD ()

Example

Tid Items

10 A,B,C,D,E

20 B,C,D,E,

30 A,C,D,F

(ABCDEF)

Items Frequency

ABCDEF 0

A 2

B 2

C 3

D 3

E 2

F 1

Min_sup=2

Max patterns:

A (BCDE)B (CDE) C (DE) E ()D (E)

Example

Tid Items

10 A,B,C,D,E

20 B,C,D,E,

30 A,C,D,F

(ABCDEF)

Items Frequency

ABCDE 1

AB 1

AC 2

AD 2

AE 1

Min_sup=2

A (BCDE)B (CDE) C (DE) E ()D (E)

AC (D) AD ()

Max patterns:

Node A

Example

Tid Items

10 A,B,C,D,E

20 B,C,D,E,

30 A,C,D,F

(ABCDEF)

Items Frequency

BCDE 2

BC

BD

BE

Min_sup=2

A (BCDE)B (CDE) C (DE) E ()D (E)

AC (D) AD ()

Max patterns:

BCDE

Node B

Example

Tid Items

10 A,B,C,D,E

20 B,C,D,E,

30 A,C,D,F

(ABCDEF)

Items Frequency

ACD 2

Min_sup=2

A (BCDE)B (CDE) C (DE) E ()D (E)

AC (D) AD ()

Max patterns:

BCDE

ACD ()

ACD

Node AC

Frequent Closed Patterns For frequent itemset X, if there exists no

item y s.t. every transaction containing X also contains y, then X is a frequent closed pattern “ab” is a frequent closed pattern

Concise rep. of freq pats Reduce # of patterns and rules N. Pasquier et al. In ICDT’99

TID Items

10 a, b, c

20 a, b, c

30 a, b, d

40 a, b, d

50 e, f

Min_sup=2

Max Pattern vs. Frequent Closed Pattern max pattern closed pattern

if itemset X is a max pattern, adding any item to it would not be a frequent pattern; thus there exists no item y s.t. every transaction containing X also contains y.

closed pattern max pattern “ab” is a closed pattern, but not max TID Items

10 a, b, c

20 a, b, c

30 a, b, d

40 a, b, d

50 e, f

Min_sup=2

Mining Frequent Closed Patterns: CLOSET

Flist: list of all frequent items in support ascending order

Flist: d-a-f-e-c

Divide search space

Patterns having d

Patterns having a but not d, etc.

Find frequent closed pattern recursively

Among the transactions having d, cfa is frequent closed cfad is a frequent closed pattern

J. Pei, J. Han & R. Mao. CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets", DMKD'00.

TID Items

10 a, c, d, e, f20 a, b, e30 c, e, f40 a, c, d, f50 c, e, f

Min_sup=2

Multiple-Level Association Rules

Items often form hierarchy. Items at the lower level are

expected to have lower support.

Rules regarding itemsets at appropriate levels could be

quite useful. A transactional database can

be encoded based on dimensions and levels

We can explore shared multi-level mining

Food

breadmilk

skim

Garelick

2% fat whitewheat

Wonder....

Mining Multi-Level Associations

A top_down, progressive deepening approach: First find high-level strong rules:

milk bread [20%, 60%]. Then find their lower-level “weaker” rules:

2% fat milk wheat bread [6%, 50%]. Variations at mining multiple-level association

rules. Level-crossed association rules:

skim milk Wonder wheat bread Association rules with multiple, alternative

hierarchies:

full fat milk Wonder bread

Multi-level Association: Uniform Support vs. Reduced Support Uniform Support: the same minimum

support for all levels + One minimum support threshold. No need to

examine itemsets containing any item whose ancestors do not have minimum support.

– Lower level items do not occur as frequently. If support threshold

too high miss low level associations too low generate too many high level

associations

Multi-level Association: Uniform Support vs. Reduced Support Reduced Support: reduced minimum

support at lower levels There are 4 search strategies:

Level-by-level independent Independent search at all levels (no misses)

Level-cross filtering by k-itemset Prune a k-pattern if the corresponding k-pattern at

the upper level is infrequent Level-cross filtering by single item

Prune an item if its parent node is infrequent Controlled level-cross filtering by single item

Consider ‘subfrequent’ items that pass a passage threshold

Uniform SupportMulti-level mining with uniform support

Milk

[support = 10%]

full fat Milk

[support = 6%]

Skim Milk

[support = 4%]

Level 1min_sup = 5%

Level 2min_sup = 5%

X

Reduced SupportMulti-level mining with reduced support

full fat Milk

[support = 6%]

Skim Milk

[support = 4%]

Level 1min_sup = 5%

Level 2min_sup = 3%

Milk

[support = 10%]

Interestingness Measurements

Objective measuresTwo popular measurements: support; and confidence

Subjective measuresA rule (pattern) is interesting if it is unexpected (surprising to the user); and/or actionable (the user can do something with it)

Criticism to Support and Confidence

Example 1: Among 5000 students

3000 play basketball 3750 eat cereal 2000 both play basket ball and eat cereal

play basketball eat cereal [40%, 66.7%] is misleading because the overall percentage of students eating cereal is 75% which is higher than 66.7%.

play basketball not eat cereal [20%, 33.3%] is far more accurate, although with lower support and confidence

basketball not basketball sum(row)cereal 2000 1750 3750not cereal 1000 250 1250sum(col.) 3000 2000 5000

Criticism to Support and Confidence (Cont.)

Example 2: X and Y: positively correlated, X and Z, negatively related support and confidence of X=>Z dominates

We need a measure of dependent or correlated events

P(B|A)/P(B) is also called the lift of rule A => B

X 1 1 1 1 0 0 0 0Y 1 1 0 0 0 0 0 0Z 0 1 1 1 1 1 1 1

Rule Support ConfidenceX=>Y 25% 50%X=>Z 37.50% 75%)()(

)(, BPAP

BAPcorr BA

Other Interestingness Measures: Interest Interest (correlation, lift)

taking both P(A) and P(B) in consideration

P(AB)=P(B)*P(A), if A and B are independent events

A and B negatively correlated, if the value is less than 1;

otherwise A and B positively correlated

)()(

)(

BPAP

BAP

X 1 1 1 1 0 0 0 0Y 1 1 0 0 0 0 0 0Z 0 1 1 1 1 1 1 1

Itemset Support InterestX,Y 25% 2X,Z 37.50% 0.9Y,Z 12.50% 0.57