Data Mining: Concepts and Techniques_ Chapter 6: Mining Frequent Patterns, Association and Correlations: Basic Concepts and Methods

1
Data Mining:
Concepts and Techniques
(3rd ed.)
— Chapter 6 —
Jiawei Han, Micheline Kamber, and Jian Pei
University of Illinois at Urbana-Champaign &
Simon Fraser University
©2013 Han, Kamber & Pei. All rights reserved.

September 14, 2014 Data Mining: Concepts and Techniques 2

Chapter 6: Mining Frequent Patterns, Association
and Correlations: Basic Concepts and Methods
3
 Basic Concepts
 Frequent Itemset Mining Methods
 Which Patterns Are Interesting?—Pattern
Evaluation Methods
 Summary

4
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
 Motivation: Finding inherent regularities in data
 What products were often purchased together?— Beer and diapers?!
 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.

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Why Is Freq. Pattern Mining Important?
 Freq. pattern: An intrinsic and important property of
datasets
 Foundation for many essential data mining tasks
 Association, correlation, and causality analysis
 Sequential, structural (e.g., sub-graph) patterns
 Pattern analysis in spatiotemporal, multimedia, time-series,
and stream data
 Classification: discriminative, frequent pattern analysis
 Cluster analysis: frequent pattern-based clustering
 Data warehousing: iceberg cube and cube-gradient
 Semantic data compression: fascicles
 Broad applications

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Basic Concepts: Frequent Patterns
 itemset: A set of one or more
items
 k-itemset X = {x1, …, xk}
 (absolute) support, or, support
count of X: Frequency or
occurrence of an itemset X
 (relative) support, s, is the
fraction of transactions that
contains X (i.e., the probability
that a transaction contains X)
 An itemset X is frequent if X’s
support is no less than a minsup
threshold
Tid Items bought
10 Beer, Nuts, Diaper
20 Beer, Coffee, Diaper
30 Beer, Diaper, Eggs
40 Nuts, Eggs, Milk
50 Nuts, Coffee, Diaper, Eggs, Milk
Customer
buys diaper
Customer
buys both
Customer
buys beer

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Basic Concepts: Association Rules
 Find all the rules X  Y with
minimum support and confidence
 support, s, probability that a
transaction contains X  Y
 confidence, c, conditional
probability that a transaction
having X also contains Y
Let minsup = 50%, minconf = 50%
Freq. Pat.: Beer:3, Nuts:3, Diaper:4, Eggs:3,
{Beer, Diaper}:3
Tid Items bought
10 Beer, Nuts, Diaper
20 Beer, Coffee, Diaper
30 Beer, Diaper, Eggs
40 Nuts, Eggs, Milk
50 Nuts, Coffee, Diaper, Eggs, Milk
Customer
buys
diaper
Customer
buys both
Customer
buys beer
 Association rules: (many more!)
 Beer  Diaper (60%, 100%)
 Diaper  Beer (60%, 75%)

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Closed Patterns and Max-Patterns
 A long pattern contains a combinatorial number of sub-patterns,
e.g., {a1, …, a100} contains (100
1) + (100
2) + … +
(1
1
0
0
0
0) = 2100 – 1 = 1.27*1030 sub-patterns!
 Solution: Mine closed patterns and max-patterns instead
 An itemset X is closed if X is frequent and there exists no
super-pattern Y כ X, with the same support as X
(proposed by Pasquier, et al. @ ICDT’99)
 An itemset X is a max-pattern if X is frequent and there
exists no frequent super-pattern Y כ X (proposed by
Bayardo @ SIGMOD’98)
 Closed pattern is a lossless compression of freq. patterns
 Reducing the # of patterns and rules

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Closed Patterns and Max-Patterns
 Exercise: Suppose a DB contains only two transactions
 <a1, …, a100>, <a1, …, a50>
 Let min_sup = 1
 What is the set of closed itemset?
 {a1, …, a100}: 1
 {a1, …, a50}: 2
 What is the set of max-pattern?
 {a1, …, a100}: 1
 What is the set of all patterns?
 {a1}: 2, …, {a1, a2}: 2, …, {a1, a51}: 1, …, {a1, a2, …, a100}: 1
 A big number: 2100 - 1? Why?

10
 Basic Concepts
Evaluation Methods
 Summary

Scalable Frequent Itemset Mining Methods
11
 Apriori: A Candidate Generation-and-Test
Approach
 Improving the Efficiency of Apriori
 FPGrowth: A Frequent Pattern-Growth Approach
 ECLAT: Frequent Pattern Mining with Vertical
Data Format

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The Downward Closure Property and Scalable
Mining Methods
 The downward closure property of frequent patterns
 Any subset of a frequent itemset must be frequent
 If {beer, diaper, nuts} is frequent, so is {beer,
diaper}
 i.e., every transaction having {beer, diaper, nuts} also
contains {beer, diaper}
 Scalable mining methods: Three major approaches
 Apriori (Agrawal & Srikant@VLDB’94)
 Freq. pattern growth (FPgrowth—Han, Pei & Yin
@SIGMOD’00)
 Vertical data format approach (Charm—Zaki & Hsiao
@SDM’02)

Apriori: A Candidate Generation & Test Approach
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 Apriori pruning principle: If there is any itemset which is
infrequent, its superset should not be generated/tested!
(Agrawal & Srikant @VLDB’94, Mannila, et al. @ KDD’ 94)
 Method:
 Initially, scan DB once to get frequent 1-itemset
 Generate length (k+1) candidate itemsets from length k
frequent itemsets
 Test the candidates against DB
 Terminate when no frequent or candidate set can be
generated

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The Apriori Algorithm—An Example
Database TDB
C1
1st scan
L1
Tid Items
10 A, C, D
20 B, C, E
30 A, B, C, E
40 B, E
L2
Itemset sup
{A} 2
{B} 3
{C} 3
{D} 1
{E} 3
C2 C2
2nd scan
C3 3rd scan L3
Itemset sup
{A} 2
{B} 3
{C} 3
{E} 3
Itemset
{A, B}
{A, C}
{A, E}
{B, C}
{B, E}
{C, E}
Itemset sup
{A, B} 1
{A, C} 2
{A, E} 1
{B, C} 2
{B, E} 3
{C, E} 2
Itemset sup
{A, C} 2
{B, C} 2
{B, E} 3
{C, E} 2
Itemset
{B, C, E}
Itemset sup
{B, C, E} 2
Supmin = 2

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The Apriori Algorithm (Pseudo-Code)
Ck: Candidate itemset of size k
Lk : 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
end
return k Lk;

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Implementation of Apriori
 How to generate candidates?
 Step 1: self-joining Lk
 Step 2: pruning
 Example of Candidate-generation
 L3={abc, abd, acd, ace, bcd}
 Self-joining: L3*L3
 abcd from abc and abd
 acde from acd and ace
 Pruning:
 acde is removed because ade is not in L3
 C4 = {abcd}

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Candidate Generation: An SQL Implementation
 SQL Implementation of candidate generation
 Suppose the items in Lk-1 are listed in an order
 Step 1: self-joining Lk-1
insert into Ck
select p.item1, p.item2, …, p.itemk-1, q.itemk-1
from Lk-1 p, Lk-1 q
where p.item1=q.item1, …, p.itemk-2=q.itemk-2, p.itemk-1 <
q.itemk-1
 Step 2: pruning
forall itemsets c in Ck do
forall (k-1)-subsets s of c do
if (s is not in Lk-1) then delete c from Ck
 Use object-relational extensions like UDFs, BLOBs, and Table functions for
efficient implementation [S. Sarawagi, S. Thomas, and R. Agrawal. Integrating
association rule mining with relational database systems: Alternatives and
implications. SIGMOD’98]

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 Apriori: A Candidate Generation-and-Test Approach
 ECLAT: Frequent Pattern Mining with Vertical Data Format
 Mining Close Frequent Patterns and Maxpatterns

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Further Improvement of the Apriori Method
 Major computational challenges
 Multiple scans of transaction database
 Huge number of candidates
 Tedious workload of support counting for candidates
 Improving Apriori: general ideas
 Reduce passes of transaction database scans
 Shrink number of candidates
 Facilitate support counting of candidates

Partition: Scan Database Only Twice
 Any itemset that is potentially frequent in DB must be
frequent in at least one of the partitions of DB
 Scan 1: partition database and find local frequent
patterns
 Scan 2: consolidate global frequent patterns
 A. Savasere, E. Omiecinski and S. Navathe, VLDB’95
DB1 + DB2 + + DBk = DB
sup1(i) < σDB1 sup2(i) < σDB2 supk(i) < σDBk sup(i) < σDB

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DHP: Reduce the Number of Candidates
 A k-itemset whose corresponding hashing bucket count is below the
threshold cannot be frequent
 Candidates: a, b, c, d, e
 Hash entries
 {ab, ad, ae}
 {bd, be, de}
 …
 Frequent 1-itemset: a, b, d, e
.
.
.
 ab is not a candidate 2-itemset if the sum of count of {ab, ad, ae}
is below support threshold
 J. Park, M. Chen, and P. Yu. An effective hash-based algorithm for
mining association rules. SIGMOD’95
count itemsets
35 {ab, ad, ae}
{yz, qs, wt}
88
102
{bd, be, de}
.
.
.
Hash Table

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Sampling for Frequent Patterns
 Select a sample of original database, mine frequent
patterns within sample using Apriori
 Scan database once to verify frequent itemsets found in
sample, only borders of closure of frequent patterns are
checked
 Example: check abcd instead of ab, ac, …, etc.
 Scan database again to find missed frequent patterns
 H. Toivonen. Sampling large databases for association
rules. In VLDB’96

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DIC: Reduce Number of Scans
ABCD
ABC ABD ACD BCD
AB AC BC AD BD CD
A B C D
{}
Itemset lattice
 Once both A and D are determined
frequent, the counting of AD begins
 Once all length-2 subsets of BCD are
determined frequent, the counting of BCD
begins
Transactions
1-itemsets
2-itemsets
…
Apriori
1-itemsets
2-items
DIC 3-items
S. Brin R. Motwani, J. Ullman,
and S. Tsur. Dynamic itemset
counting and implication rules for
market basket data. In
SIGMOD’97

26

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Pattern-Growth Approach: Mining Frequent
Patterns Without Candidate Generation
 Bottlenecks of the Apriori approach
 Breadth-first (i.e., level-wise) search
 Candidate generation and test
 Often generates a huge number of candidates
 The FPGrowth Approach (J. Han, J. Pei, and Y. Yin, SIGMOD’ 00)
 Depth-first search
 Avoid explicit candidate generation
 Major philosophy: Grow long patterns from short ones using local
frequent items only
 “abc” is a frequent pattern
 Get all transactions having “abc”, i.e., project DB on abc: DB|abc
 “d” is a local frequent item in DB|abc  abcd is a frequent pattern

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Construct FP-tree from a Transaction Database
{}
f:4 c:1
b:1
p:1
c:3 b:1
a:3
m:2 b:1
p:2 m:1
Header Table
Item frequency head
f 4
c 4
a 3
b 3
m 3
p 3
min_support = 3
TID Items bought (ordered) frequent items
100 {f, a, c, d, g, i, m, p} {f, c, a, m, p}
200 {a, b, c, f, l, m, o} {f, c, a, b, m}
300 {b, f, h, j, o, w} {f, b}
400 {b, c, k, s, p} {c, b, p}
500 {a, f, c, e, l, p, m, n} {f, c, a, m, p}
1. Scan DB once, find
frequent 1-itemset (single
item pattern)
2. Sort frequent items in
frequency descending
order, f-list
3. Scan DB again, construct
FP-tree
F-list = f-c-a-b-m-p

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Partition Patterns and Databases
 Frequent patterns can be partitioned into subsets
according to f-list
 F-list = f-c-a-b-m-p
 Patterns containing p
 Patterns having m but no p
 …
 Patterns having c but no a nor b, m, p
 Pattern f
 Completeness and non-redundency

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Find Patterns Having P From P-conditional Database
 Starting at the frequent item header table in the FP-tree
 Traverse the FP-tree by following the link of each frequent item p
 Accumulate all of transformed prefix paths of item p to form p’s
conditional pattern base
Conditional pattern bases
item cond. pattern base
c f:3
a fc:3
b fca:1, f:1, c:1
m fca:2, fcab:1
p fcam:2, cb:1
{}
f:4 c:1
b:1
p:1
c:3 b:1
a:3
m:2 b:1
p:2 m:1
Header Table
Item frequency head
f 4
c 4
a 3
b 3
m 3
p 3

From Conditional Pattern-bases to Conditional FP-trees
All frequent
patterns relate to m
m,
fm, cm, am,
fcm, fam, cam,
fcam
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 For each pattern-base
 Accumulate the count for each item in the base
 Construct the FP-tree for the frequent items of the
pattern base
m-conditional pattern base:
fca:2, fcab:1
{}
f:3
c:3
a:3


m-conditional FP-tree
{}
f:4 c:1
b:1
p:1
c:3 b:1
a:3
m:2 b:1
p:2 m:1
Header Table
Item frequency head
f 4
c 4
a 3
b 3
m 3
p 3

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Recursion: Mining Each Conditional FP-tree
{}
f:3
c:3
a:3
Cond. pattern base of “am”: (fc:3)
m-conditional FP-tree
{}
f:3
c:3
am-conditional FP-tree
Cond. pattern base of “cm”: (f:3)
{}
f:3
cm-conditional FP-tree
Cond. pattern base of “cam”: (f:3)
{}
f:3
cam-conditional FP-tree

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A Special Case: Single Prefix Path in FP-tree
 Suppose a (conditional) FP-tree T has a shared
single prefix-path P
 Mining can be decomposed into two parts
 Reduction of the single prefix path into one node
 Concatenation of the mining results of the two
parts

{}
a1:n1
a2:n2
a3:n3
b1:m1
C1:k1
C2:k2 C3:k3
r1
b1:m1
C1:k1
C2:k2 C3:k3
+
{}
a1:n1
a2:n2
a3:n3
r1 =

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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)

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The Frequent Pattern Growth Mining Method
 Idea: Frequent pattern growth
 Recursively grow frequent patterns by pattern and
database partition
 Method
 For each frequent item, construct its conditional
pattern-base, and then its conditional FP-tree
 Repeat the process on each newly created conditional
FP-tree
 Until the resulting FP-tree is empty, or it contains only
one path—single path will generate all the
combinations of its sub-paths, each of which is a
frequent pattern

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Scaling FP-growth by Database Projection
 What about if FP-tree cannot fit in memory?
 DB projection
 First partition a database into a set of projected DBs
 Then construct and mine FP-tree for each projected DB
 Parallel projection vs. partition projection techniques
 Parallel projection
 Project the DB in parallel for each frequent item
 Parallel projection is space costly
 All the partitions can be processed in parallel
 Partition projection
 Partition the DB based on the ordered frequent items
 Passing the unprocessed parts to the subsequent partitions

37
Partition-Based Projection
 Parallel projection needs a lot
of disk space
 Partition projection saves it
Tran. DB
fcamp
fcabm
fb
cbp
fcamp
p-proj DB
fcam
cb
fcam
m-proj DB
fcab
fca
fca
b-proj DB
f
cb
…
a-proj DB
fc
…
c-proj DB
f
…
f-proj DB
…
am-proj DB
fc
fc
fc
cm-proj DB
f
f
f
…

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FP-Growth vs. Apriori: Scalability With the Support
Threshold
100
90
80
70
60
50
40
30
20
10
0
Data set T25I20D10K
0 0.5 1 1.5 2 2.5 3
Support threshold(%)
Run time(sec.)
D1 FP-grow th runtime
D1 Apriori runtime

FP-Growth vs. Tree-Projection: Scalability with
the Support Threshold
Data Mining: Concepts and Techniques 39
140
120
100
80
60
40
20
0
0 0.5 1 1.5 2
Support threshold (%)
Runtime (sec.)
D2 FP-growth
D2 TreeProjection
Data set T25I20D100K

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Advantages of the Pattern Growth Approach
 Divide-and-conquer:
 Decompose both the mining task and DB according to the
frequent patterns obtained so far
 Lead 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
 A good open-source implementation and refinement of FPGrowth
 FPGrowth+ (Grahne and J. Zhu, FIMI'03)

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Further Improvements of Mining Methods
 AFOPT (Liu, et al. @ KDD’03)
 A “push-right” method for mining condensed frequent pattern
(CFP) tree
 Carpenter (Pan, et al. @ KDD’03)
 Mine data sets with small rows but numerous columns
 Construct a row-enumeration tree for efficient mining
 FPgrowth+ (Grahne and Zhu, FIMI’03)
 Efficiently Using Prefix-Trees in Mining Frequent Itemsets, Proc.
ICDM'03 Int. Workshop on Frequent Itemset Mining
Implementations (FIMI'03), Melbourne, FL, Nov. 2003
 TD-Close (Liu, et al, SDM’06)

Extension of Pattern Growth Mining Methodology
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 Mining closed frequent itemsets and max-patterns
 CLOSET (DMKD’00), FPclose, and FPMax (Grahne & Zhu, Fimi’03)
 Mining sequential patterns
 PrefixSpan (ICDE’01), CloSpan (SDM’03), BIDE (ICDE’04)
 Mining graph patterns
 gSpan (ICDM’02), CloseGraph (KDD’03)
 Constraint-based mining of frequent patterns
 Convertible constraints (ICDE’01), gPrune (PAKDD’03)
 Computing iceberg data cubes with complex measures
 H-tree, H-cubing, and Star-cubing (SIGMOD’01, VLDB’03)
 Pattern-growth-based Clustering
 MaPle (Pei, et al., ICDM’03)
 Pattern-Growth-Based Classification
 Mining frequent and discriminative patterns (Cheng, et al, ICDE’07)

43

ECLAT: Mining by Exploring Vertical Data Format
44
 Vertical format: t(AB) = {T11, T25, …}
 tid-list: list of trans.-ids containing an itemset
 Deriving frequent patterns based on vertical intersections
 t(X) = t(Y): X and Y always happen together
 t(X)  t(Y): transaction having X always has Y
 Using diffset to accelerate mining
 Only keep track of differences of tids
 t(X) = {T1, T2, T3}, t(XY) = {T1, T3}
 Diffset (XY, X) = {T2}
 Eclat (Zaki et al. @KDD’97)
 Mining Closed patterns using vertical format: CHARM (Zaki &
Hsiao@SDM’02)

45

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 d but no a, etc.
 Find frequent closed pattern recursively
Min_sup=2
 Every transaction having d also has cfa  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, f
20 a, b, e
30 c, e, f
40 a, c, d, f
50 c, e, f

CLOSET+: Mining Closed Itemsets by Pattern-Growth
 Itemset merging: if Y appears in every occurrence of X, then Y
is merged with X
 Sub-itemset pruning: if Y כ X, and sup(X) = sup(Y), X and all of
X’s descendants in the set enumeration tree can be pruned
 Hybrid tree projection
 Bottom-up physical tree-projection
 Top-down pseudo tree-projection
 Item skipping: if a local frequent item has the same support in
several header tables at different levels, one can prune it from
the header table at higher levels
 Efficient subset checking

MaxMiner: Mining Max-Patterns
 1st scan: find frequent items
 A, B, C, D, E
 2nd scan: find support for
 AB, AC, AD, AE, ABCDE
 BC, BD, BE, BCDE
 CD, CE, CDE, DE
 Since BCDE is a max-pattern, no need to check BCD, BDE,
CDE in later scan
 R. Bayardo. Efficiently mining long patterns from
databases. SIGMOD’98
Tid Items
10 A, B, C, D, E
20 B, C, D, E,
30 A, C, D, F
Potential
max-patterns

CHARM: Mining by Exploring Vertical Data
Format
 Vertical format: t(AB) = {T11, T25, …}
 tid-list: list of trans.-ids containing an itemset
 Deriving closed patterns based on vertical intersections
 t(X) = t(Y): X and Y always happen together
 t(X)  t(Y): transaction having X always has Y
 Using diffset to accelerate mining
 Only keep track of differences of tids
 t(X) = {T1, T2, T3}, t(XY) = {T1, T3}
 Diffset (XY, X) = {T2}
 Eclat/MaxEclat (Zaki et al. @KDD’97), VIPER(P. Shenoy et
al.@SIGMOD’00), CHARM (Zaki & Hsiao@SDM’02)

50
Visualization of Association Rules: Plane Graph

51
Visualization of Association Rules: Rule Graph

52
Visualization of Association Rules
(SGI/MineSet 3.0)

53
Computational Complexity of Frequent Itemset
Mining
 How many itemsets are potentially to be generated in the worst case?
 The number of frequent itemsets to be generated is senstive to the
minsup threshold
 When minsup is low, there exist potentially an exponential number of
frequent itemsets
 The worst case: MN where M: # distinct items, and N: max length of
transactions
 The worst case complexty vs. the expected probability
 Ex. Suppose Walmart has 104 kinds of products
 The chance to pick up one product 10-4
 The chance to pick up a particular set of 10 products: ~10-40
 What is the chance this particular set of 10 products to be frequent
103 times in 109 transactions?

54
 Basic Concepts
Evaluation Methods
 Summary

55
Interestingness Measure: Correlations (Lift)
 play basketball  eat cereal [40%, 66.7%] is misleading
 The overall % of students eating cereal is 75% > 66.7%.
 play basketball  not eat cereal [20%, 33.3%] is more accurate,
although with lower support and confidence
 Measure of dependent/correlated events: lift
0.89
P A B
( )
P A P B
2000/ 5000
lif t(B,C)  
3000/ 5000*3750/ 5000
Basketball Not basketball Sum (row)
Cereal 2000 1750 3750
Not cereal 1000 250 1250
Sum(col.) 3000 2000 5000
( ) ( )
lift


1.33
1000/ 5000
lif t(B,C)  
3000/ 5000*1250/ 5000

56
Are lift and 2 Good Measures of Correlation?
 “Buy walnuts  buy
milk [1%, 80%]” is
misleading if 85% of
customers buy milk
 Support and confidence
are not good to indicate
correlations
 Over 20 interestingness
measures have been
proposed (see Tan,
Kumar, Sritastava
@KDD’02)
 Which are good ones?

Comparison of Interestingness Measures
 Null-(transaction) invariance is crucial for correlation analysis
 Lift and 2 are not null-invariant
 5 null-invariant measures
Milk No Milk Sum (row)
Coffee m, c ~m, c c
No Coffee m, ~c ~m, ~c ~c
Sum(col.) m ~m 
Kulczynski
measure (1927)
Null-transactions
w.r.t. m and c Null-invariant
Subtle: They disagree

59
Analysis of DBLP Coauthor Relationships
Recent DB conferences, removing balanced associations, low sup, etc.
Advisor-advisee relation: Kulc: high,
coherence: low, cosine: middle
 Tianyi Wu, Yuguo Chen and Jiawei Han, “Association Mining in Large
Databases: A Re-Examination of Its Measures”, Proc. 2007 Int. Conf.
Principles and Practice of Knowledge Discovery in Databases
(PKDD'07), Sept. 2007

Which Null-Invariant Measure Is Better?
 IR (Imbalance Ratio): measure the imbalance of two
itemsets A and B in rule implications
 Kulczynski and Imbalance Ratio (IR) together present a
clear picture for all the three datasets D4 through D6
 D4 is balanced & neutral
 D5 is imbalanced & neutral
 D6 is very imbalanced & neutral

61
 Basic Concepts
Evaluation Methods
 Summary

62
Summary
 Basic concepts: association rules, support-confident
framework, closed and max-patterns
 Scalable frequent pattern mining methods
 Apriori (Candidate generation & test)
 Projection-based (FPgrowth, CLOSET+, ...)
 Vertical format approach (ECLAT, CHARM, ...)
 Which patterns are interesting?
 Pattern evaluation methods

64
Ref: Basic Concepts of Frequent Pattern Mining
 (Association Rules) R. Agrawal, T. Imielinski, and A. Swami. Mining
association rules between sets of items in large databases.
SIGMOD'93.
 (Max-pattern) R. J. Bayardo. Efficiently mining long patterns from
databases. SIGMOD'98.
 (Closed-pattern) N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal.
Discovering frequent closed itemsets for association rules. ICDT'99.
 (Sequential pattern) R. Agrawal and R. Srikant. Mining sequential
patterns. ICDE'95

65
Ref: Apriori and Its Improvements
 R. Agrawal and R. Srikant. Fast algorithms for mining association rules.
VLDB'94.
 H. Mannila, H. Toivonen, and A. I. Verkamo. Efficient algorithms for
discovering association rules. KDD'94.
 A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for
mining association rules in large databases. VLDB'95.
 J. S. Park, M. S. Chen, and P. S. Yu. An effective hash-based algorithm
for mining association rules. SIGMOD'95.
 H. Toivonen. Sampling large databases for association rules. VLDB'96.
 S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset
counting and implication rules for market basket analysis. SIGMOD'97.
 S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule
mining with relational database systems: Alternatives and implications.
SIGMOD'98.

66
Ref: Depth-First, Projection-Based FP Mining
 R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for
generation of frequent itemsets. J. Parallel and Distributed Computing:02.
 J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate
generation. SIGMOD’ 00.
 J. Liu, Y. Pan, K. Wang, and J. Han. Mining Frequent Item Sets by
Opportunistic Projection. KDD'02.
 J. Han, J. Wang, Y. Lu, and P. Tzvetkov. Mining Top-K Frequent Closed
Patterns without Minimum Support. ICDM'02.
 J. Wang, J. Han, and J. Pei. CLOSET+: Searching for the Best Strategies for
Mining Frequent Closed Itemsets. KDD'03.
 G. Liu, H. Lu, W. Lou, J. X. Yu. On Computing, Storing and Querying Frequent
Patterns. KDD'03.
 G. Grahne and J. Zhu, Efficiently Using Prefix-Trees in Mining Frequent
Itemsets, Proc. ICDM'03 Int. Workshop on Frequent Itemset Mining
Implementations (FIMI'03), Melbourne, FL, Nov. 2003

67
Ref: Vertical Format and Row Enumeration Methods
 M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. Parallel algorithm
for discovery of association rules. DAMI:97.
 Zaki and Hsiao. CHARM: An Efficient Algorithm for Closed Itemset
Mining, SDM'02.
 C. Bucila, J. Gehrke, D. Kifer, and W. White. DualMiner: A Dual-
Pruning Algorithm for Itemsets with Constraints. KDD’02.
 F. Pan, G. Cong, A. K. H. Tung, J. Yang, and M. Zaki , CARPENTER:
Finding Closed Patterns in Long Biological Datasets. KDD'03.
 H. Liu, J. Han, D. Xin, and Z. Shao, Mining Interesting Patterns from
Very High Dimensional Data: A Top-Down Row Enumeration
Approach, SDM'06.

68
Ref: Mining Correlations and Interesting Rules
 M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I.
Verkamo. Finding interesting rules from large sets of discovered
association rules. CIKM'94.
 S. Brin, R. Motwani, and C. Silverstein. Beyond market basket:
Generalizing association rules to correlations. SIGMOD'97.
 C. Silverstein, S. Brin, R. Motwani, and J. Ullman. Scalable
techniques for mining causal structures. VLDB'98.
 P.-N. Tan, V. Kumar, and J. Srivastava. Selecting the Right
Interestingness Measure for Association Patterns. KDD'02.
 E. Omiecinski. Alternative Interest Measures for Mining
Associations. TKDE’03.
 T. Wu, Y. Chen and J. Han, “Association Mining in Large Databases:
A Re-Examination of Its Measures”, PKDD'07

69
Ref: Freq. Pattern Mining Applications
 Y. Huhtala, J. Kärkkäinen, P. Porkka, H. Toivonen. Efficient
Discovery of Functional and Approximate Dependencies Using
Partitions. ICDE’98.
 H. V. Jagadish, J. Madar, and R. Ng. Semantic Compression and
Pattern Extraction with Fascicles. VLDB'99.
 T. Dasu, T. Johnson, S. Muthukrishnan, and V. Shkapenyuk.
Mining Database Structure; or How to Build a Data Quality
Browser. SIGMOD'02.
 K. Wang, S. Zhou, J. Han. Profit Mining: From Patterns to Actions.
EDBT’02.

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