ch5 mining frequent patterns, associations, and correlations dr. bernard chen ph.d. university of...

Ch5 Mining Frequent Patterns, Associations, and Correlations

Dr. Bernard Chen Ph.D.University of Central Arkansas

Outline

Association Rules Association Rules with FP tree Misleading Rules Multi-level Association Rules

What Is Frequent Pattern Analysis? Frequent pattern: a pattern (a set of

items, subsequences, substructures, etc.) that occurs frequently in a data set

First proposed by Agrawal, Imielinski,

and Swami [AIS93] in the context of

frequent itemsets and association rule

mining

What Is Frequent Pattern Analysis?

Motivation: Finding inherent regularities in data What products were often purchased together? bread and

milk?

What are the subsequent purchases after buying a PC?

What kinds of DNA are sensitive to this new drug?

Can we automatically classify web documents?

Applications

Basket data analysis, cross-marketing, catalog design, sale

campaign analysis, Web log (click stream) analysis, and DNA

sequence analysis.

Association Rules

Association Rules

support, s, probability that a transaction contains X Y

confidence, c, conditional probability that a transaction having X also contains Y

Association Rules Let’s have an example

T100 1,2,5 T200 2,4 T300 2,3 T400 1,2,4 T500 1,3 T600 2,3 T700 1,3 T800 1,2,3,5 T900 1,2,3

Association Rules with AprioriMinimum support=2/9Minimum confidence=60%

The Apriori Algorithm

Pseudo-code:Ck: Candidate itemset of size kLk : frequent itemset of size k

L1 = {frequent items};for (k = 1; Lk !=; k++) do begin Ck+1 = candidates generated from Lk; for each transaction t in database do

increment the count of all candidates in Ck+1 that are contained in t

Lk+1 = candidates in Ck+1 with min_support endreturn k Lk;

Strong Association Rule

Strong association rules means the frequent rules that also pass the minimum confidence.

For example frequent rules: {I1, I2} Confidence(I1->I2)= 4/6

(strong association rule!) Confidence(I2->I1)= 4/7

Exercise A dataset has five

transactions, let min-support=60% and min_support=80%

Find all frequent itemsets using Apriori and all strong association rules

TID Items_bought

T1T2T3T4T5

M, O, N, K, E, YD, O, N, K , E, YM, A, K, EM, U, C, K ,YC, O, O, K, I ,E

Association Rules with AprioriK:5 KE:4 KEE:4 KM:3 KMM:3 KO:3 KOO:3 => KY:3 => KY => KEOY:3 EM:2 EO

EO:3EY:2MO:1MY:2OY:2

Outline


Mining Frequent Itemsets without Candidate Generation In many cases, the Apriori candidate

generate-and-test method significantly reduces the size of candidate sets, leading to good performance gain.

However, it suffer from two nontrivial costs: It may generate a huge number of candidates

(for example, if we have 10^4 1-itemset, it may generate more than 10^7 candidata 2-itemset)

It may need to scan database many times

Association Rules with AprioriMinimum support=2/9Minimum confidence=70%

Bottleneck of Frequent-pattern Mining

Multiple database scans are costly Mining long patterns needs many passes of

scanning and generates lots of candidates To find frequent itemset i1i2…i100

# of scans: 100 # of Candidates: (100

1) + (1002) + … + (1

10

00

0) =

2100-1 = 1.27*1030 ! Bottleneck: candidate-generation-and-test Can we avoid candidate generation?

Mining Frequent Patterns Without Candidate Generation

Grow long patterns from short ones using local

frequent items

“abc” is a frequent pattern

Get all transactions having “abc”: DB|abc

“d” is a local frequent item in DB|abc abcd is a

frequent pattern

Process of FP growth

Scan DB once, find frequent 1-itemset (single item pattern)

Sort frequent items in frequency descending order

Scan DB again, construct FP-tree

Association Rules Let’s have an example

T100 1,2,5 T200 2,4 T300 2,3 T400 1,2,4 T500 1,3 T600 2,3 T700 1,3 T800 1,2,3,5 T900 1,2,3

FP Tree

Mining the FP tree

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

Exercise A dataset has five

transactions, let min-support=60% and min_confidence=80%

Find all frequent itemsets using FP Tree

TID Items_bought

T1T2T3T4T5

M, O, N, K, E, YD, O, N, K , E, YM, A, K, EM, U, C, K ,YC, O, O, K, I ,E

Association Rules with FP Tree

K:5E:4M:3O:3Y:3

Association Rules with FP Tree

Y: KEMO:1 KEO:1 KY:1 K:3 KY

O: KEM:1 KE:2 KE:3 KO EO KEOM: KE:2 K:1 K:3 KME: K:4 KE

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(%)

Ru

n t

ime

(se

c.)

D1 FP-grow th runtime

D1 Apriori runtime

Data set T25I20D10K

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

Outline


Example 5.8 Misleading “Strong” Association Rule

Of the 10,000 transactions analyzed, the data show that 6,000 of the customer included

computer games, while 7,500 include videos, And 4,000 included both computer

games and videos

Misleading “Strong” Association Rule For this example:

Support (Game & Video) = 4,000 / 10,000 =40%

Confidence (Game => Video) = 4,000 / 6,000 = 66%

Suppose it pass our minimum support and confidence (30% , 60%, respectively)

Misleading “Strong” Association Rule

However, the truth is : “computer games and videos are negatively associated”

Which means the purchase of one of these items actually decreases the likelihood of purchasing the other.

(How to get this conclusion??)

Misleading “Strong” Association Rule

Under the normal situation, 60% of customers buy the game 75% of customers buy the video Therefore, it should have 60% * 75%

= 45% of people buy both That equals to 4,500 which is more

than 4,000 (the actual value)

From Association Analysis to Correlation Analysis Lift is a simple correlation measure that is

given as follows The occurrence of itemset A is independent of the

occurrence of itemset B ifP(AUB) = P(A)P(B)

Otherwise, itemset A and B are dependent and correlated as events

Lift(A,B) = P(AUB) / P(A)P(B) If the value is less than 1, the occurrence of A is

negatively correlated with the occurrence of B If the value is greater than 1, then A and B are

positively correlated

Outline


Mining Multiple-Level Association Rules

Items often form hierarchies

Mining Multiple-Level Association Rules

Flexible support settings Items at the lower level are expected

to have lower support

uniform support

Milk[support = 10%]

2% Milk [support = 6%]

Skim Milk [support = 4%]

Level 1min_sup = 5%

Level 2min_sup = 5%

Level 1min_sup = 5%

Level 2min_sup = 3%

reduced support

Multi-level Association: Redundancy Filtering

Some rules may be redundant due to “ancestor” relationships between items.

Example milk wheat bread [support = 8%, confidence = 70%]

2% milk wheat bread [support = 2%, confidence = 72%]

We say the first rule is an ancestor of the second rule.

ch5 mining frequent patterns, associations, and correlations dr. bernard chen ph.d. university of...

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