Transcript 1416274680chap6_basi..

Data Mining
Association Analysis: Basic Concepts
and Algorithms
Lecture Notes for Chapter 6
Introduction to Data Mining
by
Tan, Steinbach, Kumar
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
Association Rule Mining

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
Market-Basket transactions
TID
Items
1
Bread, Milk
2
3
4
5
Bread, Diaper, Beer, Eggs
Milk, Diaper, Beer, Coke
Bread, Milk, Diaper, Beer
Bread, Milk, Diaper, Coke
© Tan,Steinbach, Kumar
Introduction to Data Mining
Example of Association Rules
{Diaper}  {Beer},
{Milk, Bread}  {Eggs,Coke},
{Beer, Bread}  {Milk},
Implication means co-occurrence,
not causality!
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Definition: Frequent Itemset

Itemset
– A collection of one or more items

Example: {Milk, Bread, Diaper}
– k-itemset


An itemset that contains k items
Support count ()
– Frequency of occurrence of an itemset
– E.g. ({Milk, Bread,Diaper}) = 2

Support
TID
Items
1
Bread, Milk
2
3
4
5
Bread, Diaper, Beer, Eggs
Milk, Diaper, Beer, Coke
Bread, Milk, Diaper, Beer
Bread, Milk, Diaper, Coke
– Fraction of transactions that contain an
itemset
– E.g. s({Milk, Bread, Diaper}) = 2/5

Frequent Itemset
– An itemset whose support is greater
than or equal to a minsup threshold
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
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Definition: Association Rule

Association Rule
– An implication expression of the form
X  Y, where X and Y are itemsets
– Example:
{Milk, Diaper}  {Beer}

Rule Evaluation Metrics
TID
Items
1
Bread, Milk
2
3
4
5
Bread, Diaper, Beer, Eggs
Milk, Diaper, Beer, Coke
Bread, Milk, Diaper, Beer
Bread, Milk, Diaper, Coke
– Support (s)

Example:
Fraction of transactions that contain
both X and Y
{Milk , Diaper }  Beer
– Confidence (c)

Measures how often items in Y
appear in transactions that
contain X
© Tan,Steinbach, Kumar
s
 (Milk, Diaper, Beer )
|T|

2
 0.4
5
 (Milk, Diaper, Beer ) 2
c
  0.67
 (Milk, Diaper )
3
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Association Rule Mining Task

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
 Computationally prohibitive!
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Mining Association Rules
Example of Rules:
TID
Items
1
Bread, Milk
2
3
4
5
Bread, Diaper, Beer, Eggs
Milk, Diaper, Beer, Coke
Bread, Milk, Diaper, Beer
Bread, Milk, Diaper, Coke
{Milk,Diaper}  {Beer} (s=0.4, c=0.67)
{Milk,Beer}  {Diaper} (s=0.4, c=1.0)
{Diaper,Beer}  {Milk} (s=0.4, c=0.67)
{Beer}  {Milk,Diaper} (s=0.4, c=0.67)
{Diaper}  {Milk,Beer} (s=0.4, c=0.5)
{Milk}  {Diaper,Beer} (s=0.4, c=0.5)
Observations:
• All the above rules are binary partitions of the same itemset:
{Milk, Diaper, Beer}
• Rules originating from the same itemset have identical support but
can have different confidence
• Thus, we may decouple the support and confidence requirements
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Mining Association Rules

Two-step approach:
1. Frequent Itemset Generation
–
Generate all itemsets whose support  minsup
2. Rule Generation
–

Generate high confidence rules from each frequent itemset,
where each rule is a binary partitioning of a frequent itemset
Frequent itemset generation is still
computationally expensive
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
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Frequent Itemset Generation
null
A
B
C
D
E
AB
AC
AD
AE
BC
BD
BE
CD
CE
DE
ABC
ABD
ABE
ACD
ACE
ADE
BCD
BCE
BDE
CDE
ABCD
ABCE
ABDE
ACDE
ABCDE
© Tan,Steinbach, Kumar
Introduction to Data Mining
BCDE
Given d items, there
are 2d possible
candidate itemsets
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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
Transactions
N
TID
1
2
3
4
5
Items
Bread, Milk
Bread, Diaper, Beer, Eggs
Milk, Diaper, Beer, Coke
Bread, Milk, Diaper, Beer
Bread, Milk, Diaper, Coke
List of
Candidates
M
w
– Match each transaction against every candidate
– Complexity ~ O(NMw) => Expensive since M = 2d !!!
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
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Reducing 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:
X , Y : ( X  Y )  s( X )  s(Y )
– Support of an itemset never exceeds the support of its
subsets
– This is known as the anti-monotone property of support
© Tan,Steinbach, Kumar
Introduction to Data Mining
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‹#›
Illustrating Apriori Principle
null
A
B
C
D
E
AB
AC
AD
AE
BC
BD
BE
CD
CE
DE
ABC
ABD
ABE
ACD
ACE
ADE
BCD
BCE
BDE
CDE
Found to be
Infrequent
ABCD
ABCE
Pruned
supersets
© Tan,Steinbach, Kumar
Introduction to Data Mining
ABDE
ACDE
BCDE
ABCDE
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Illustrating Apriori Principle
Item
Bread
Coke
Milk
Beer
Diaper
Eggs
Count
4
2
4
3
4
1
Items (1-itemsets)
Itemset
{Bread,Milk}
{Bread,Beer}
{Bread,Diaper}
{Milk,Beer}
{Milk,Diaper}
{Beer,Diaper}
Minimum Support = 3
Pairs (2-itemsets)
(No need to generate
candidates involving Coke
or Eggs)
Triplets (3-itemsets)
If every subset is considered,
6C + 6C + 6C = 41
1
2
3
With support-based pruning,
6 + 6 + 1 = 13
© Tan,Steinbach, Kumar
Count
3
2
3
2
3
3
Introduction to Data Mining
Itemset
{Bread,Milk,Diaper}
Count
3
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Apriori Algorithm

Method:
– 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
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Reducing Number of Comparisons

Candidate counting:
– Scan the database of transactions to determine the
support of each candidate itemset
– To reduce the number of comparisons, store the
candidates in a hash structure
Instead of matching each transaction against every candidate,
match it against candidates contained in the hashed buckets

Transactions
N
TID
1
2
3
4
5
Hash Structure
Items
Bread, Milk
Bread, Diaper, Beer, Eggs
Milk, Diaper, Beer, Coke
Bread, Milk, Diaper, Beer
Bread, Milk, Diaper, Coke
k
Buckets
© Tan,Steinbach, Kumar
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4/18/2004
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Factors Affecting Complexity

Choice of minimum support threshold
–
–

Dimensionality (number of items) of the data set
–
–

more space is needed to store support count of each item
if number of frequent items also increases, both computation and
I/O costs may also increase
Size of database
–

lowering support threshold results in more frequent itemsets
this may increase number of candidates and max length of
frequent itemsets
since Apriori makes multiple passes, run time of algorithm may
increase with number of transactions
Average transaction width
– transaction width increases with denser data sets
– This may increase max length of frequent itemsets (number of
subsets in a transaction increases with its width)
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
FP-growth Algorithm

Use a compressed representation of the
database using an FP-tree

Once an FP-tree has been constructed, it uses a
recursive divide-and-conquer approach to mine
the frequent itemsets
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
FP-tree construction
null
After reading TID=1:
TID
1
2
3
4
5
6
7
8
9
10
Items
{A,B}
{B,C,D}
{A,C,D,E}
{A,D,E}
{A,B,C}
{A,B,C,D}
{B,C}
{A,B,C}
{A,B,D}
{B,C,E}
A:1
B:1
After reading TID=2:
null
A:1
B:1
B:1
C:1
D:1
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
FP-Tree Construction
TID
1
2
3
4
5
6
7
8
9
10
Items
{A,B}
{B,C,D}
{A,C,D,E}
{A,D,E}
{A,B,C}
{A,B,C,D}
{B,C}
{A,B,C}
{A,B,D}
{B,C,E}
Header table
Item
Pointer
A
B
C
D
E
© Tan,Steinbach, Kumar
Transaction
Database
null
B:3
A:7
B:5
C:1
C:3
D:1
D:1
C:3
D:1
D:1
D:1
E:1
E:1
E:1
Pointers are used to assist
frequent itemset generation
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FP-growth
C:1
Conditional Pattern base
for D:
P = {(A:1,B:1,C:1),
(A:1,B:1),
(A:1,C:1),
(A:1),
(B:1,C:1)}
D:1
Recursively apply FPgrowth on P
null
A:7
B:5
B:1
C:1
C:3
D:1
D:1
Frequent Itemsets found
(with sup > 1):
AD, BD, CD, ACD, BCD
D:1
D:1
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
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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,
A BCD,
AB CD,
BD AC,

ABD C,
B ACD,
AC  BD,
CD AB,
ACD B,
C ABD,
AD  BC,
BCD A,
D ABC
BC AD,
If |L| = k, then there are 2k – 2 candidate
association rules (ignoring L   and   L)
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Rule Generation

How to efficiently generate rules from frequent
itemsets?
– In general, confidence does not have an antimonotone 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
– e.g., L = {A,B,C,D}:
c(ABC  D)  c(AB  CD)  c(A  BCD)
Confidence is anti-monotone w.r.t. number of items on the
RHS of the rule

© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Rule Generation for Apriori Algorithm
Lattice of rules
Low
Confidence
Rule
CD=>AB
ABCD=>{ }
BCD=>A
ACD=>B
BD=>AC
D=>ABC
BC=>AD
C=>ABD
ABD=>C
AD=>BC
B=>ACD
ABC=>D
AC=>BD
AB=>CD
A=>BCD
Pruned
Rules
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
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Rule Generation for Apriori Algorithm

Candidate rule is generated by merging two rules
that share the same prefix
in the rule consequent
CD=>AB

BD=>AC
join(CD=>AB,BD=>AC)
would produce the candidate
rule D => ABC
D=>ABC

Prune rule D=>ABC if its
subset AD=>BC does not have
high confidence
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
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Effect of Support Distribution

Many real data sets have skewed support
distribution
Support
distribution of
a retail data set
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
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Effect of Support Distribution

How to set the appropriate minsup threshold?
– 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

Using a single minimum support threshold may
not be effective
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Multiple Minimum Support

How to apply multiple minimum supports?
– MS(i): minimum support for item i
– e.g.: MS(Milk)=5%,
MS(Coke) = 3%,
MS(Broccoli)=0.1%, MS(Salmon)=0.5%
– MS({Milk, Broccoli}) = min (MS(Milk), MS(Broccoli))
= 0.1%
– Challenge: Support is no longer anti-monotone


Suppose:
Support(Milk, Coke) = 1.5% and
Support(Milk, Coke, Broccoli) = 0.5%
{Milk,Coke} is infrequent but {Milk,Coke,Broccoli} is frequent
© Tan,Steinbach, Kumar
Introduction to Data Mining
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Multiple Minimum Support (Liu 1999)

Order the items according to their minimum
support (in ascending order)
– e.g.:
MS(Milk)=5%,
MS(Coke) = 3%,
MS(Broccoli)=0.1%, MS(Salmon)=0.5%
– Ordering: Broccoli, Salmon, Coke, Milk

Need to modify Apriori such that:
– L1 : set of frequent items
– F1 : set of items whose support is  MS(1)
where MS(1) is mini( MS(i) )
– C2 : candidate itemsets of size 2 is generated from F1
instead of L1
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
‹#›
Multiple Minimum Support (Liu 1999)

Modifications to Apriori:
– In traditional Apriori,
A candidate (k+1)-itemset is generated by merging two
frequent itemsets of size k
 The candidate is pruned if it contains any infrequent subsets
of size k

– Pruning step has to be modified:
Prune only if subset contains the first item
 e.g.: Candidate={Broccoli, Coke, Milk} (ordered according to
minimum support)
 Let {Broccoli, Coke} and {Broccoli, Milk} are frequent but
{Coke, Milk} is infrequent

– Candidate is not pruned because {Coke,Milk} does not contain
the first item, i.e., Broccoli.
© Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
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