Download ppt

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
©2011 Han, Kamber & Pei. All rights reserved.
1
April 29, 2017
Data Mining: Concepts and Techniques
2
Chapter 6: Mining Frequent Patterns, Association
and Correlations: Basic Concepts and Methods
 Basic Concepts
 Frequent Itemset Mining Methods
 Which Patterns Are Interesting?—Pattern
Evaluation Methods
 Summary
3
What Is Association Mining? Or 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
Association rule mining:
 Finding frequent patterns, associations, correlations, or causal
structures among sets of items or objects in transaction
databases, relational databases, and other information
repositories.
Applications:
 Basket data analysis, cross-marketing, catalog design, loss-leader
analysis, clustering, classification, etc.
Examples.
 Rule form: “Body ead [support, confidence]”.
 buys(x, “diapers”)  buys(x, “beers”) [0.5%, 60%]
 major(x, “CS”) ^ takes(x, “DB”) grade(x, “A”) [1%, 75%]
April 29, 2017
Data Mining: Concepts and Techniques
4
Basket data analysis
April 29, 2017
Data Mining: Concepts and Techniques
5
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, timeseries, 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
6
Association Rule: Basic Concepts


Given: (1) database of transactions, (2) each transaction is
a list of items (purchased by a customer in a visit)
Find: all rules that correlate the presence of one set of
items with that of another set of items
 E.g., 98% of people who purchase tires and auto
accessories also get automotive services done

Applications




*  Maintenance Agreement (What the store should
do to boost Maintenance Agreement sales)
Home Electronics  * (What other products should
the store stocks up?)
Attached mailing in direct marketing
Detecting “ping-pong”ing of patients, faulty “collisions”
April 29, 2017
Data Mining: Concepts and Techniques
7
Association Rule Mining: A Road Map
Boolean vs. quantitative associations (Based on the types of values handled)
 buys(x, “SQLServer”) ^ buys(x, “DMBook”) buys(x, “DBMiner”) [0.2%,
60%]
 age(x, “30..39”) ^ income(x, “42..48K”) buys(x, “PC”) [1%, 75%]
 Single dimension vs. multiple dimensional associations (see ex. Above)
 buys(x, “SQLServer”)buys(x, “DMBook”)
 Single level vs. multiple-level analysis
 What brands of beers are associated with what brands of diapers?




 Buys(computer)
Age(X,31..40)  Buys(loptop_computer)
Age(X,31..40)
Various extensions
 Correlation, causality analysis



Association does not necessarily imply correlation or causality
Maxpatterns and closed itemsets
Constraints enforced

April 29, 2017
E.g., small sales (sum < 100) trigger big buys (sum > 1,000)?
Data Mining: Concepts and Techniques
8
Rule Measures: Support and Confidence
Transaction-id
Items bought
10
A, B, C
20
A, C
30
A, D
40
B, E, F
Customer
buys both
Customer
buys beer
Customer
buys diaper


Itemset X={x1, …, xk}
Find all the rules XY with min
confidence and support


support, s, probability that
a transaction contains XY
confidence, c, conditional
probability that a
transaction having X also
contains Y.
Let min_support = 50%,
min_conf = 50%:
A  C (50%, 66.7%)
C  A (50%, 100%)
9
Mining Association Rules:
Example
Transaction-id
Items bought
10
A, B, C
20
A, C
30
A, D
40
B, E, F
Min. support 50%
Min. confidence 50%
Frequent pattern
Support
{A}
75%
{B}
50%
{C}
50%
{A, C}
50%
For rule A  C:
support = support({A}{C}) = 50%
confidence = support({A}{C})/support({A})
= 66.7%
April 29, 2017
Data Mining: Concepts and Techniques
10
Association Rules Example
I = { Beer, Bread, Jelly, Milk, PeanutButter}
Support of {Bread,PeanutButter} is 60%
Support count of {Bread,PeanutButter} is 3
April 29, 2017
Data Mining: Concepts and Techniques
11
Association Rules Ex (cont’d)

Association Rule (AR): implication X  Y where X,Y  I and

Support of AR (s) X  Y: Percentage of transactions that

Confidence of AR (a) X  Y: Ratio of number of
X  Y = {} ;
contain X Y
transactions that contain X  Y to the number that contain X
April 29, 2017
Data Mining: Concepts and Techniques
12
Chapter 6: Mining Association Rules
in Large Databases




Association rule mining
Mining single-dimensional Boolean association rules
from transactional databases
Mining multilevel association rules from transactional
databases
Mining multidimensional association rules from
transactional databases and data warehouse

From association mining to correlation analysis

Constraint-based association mining

Summary
April 29, 2017
Data Mining: Concepts and Techniques
13
Association Rule Mining Problem


Given: (1) database of transactions, (2) each transaction
is a list of items (purchased by a customer in a visit)
Find Association rules from database transcactions



computer  financial management software [support = 2%;
confidence = 60%]
Typically, association rules are considered interesting if
they satisfy both a minimum support (threshold) and
a minimum confidence (threshold).
Such thresholds can be set by users or domain experts.
April 29, 2017
Data Mining: Concepts and Techniques
14
Basic Concepts




Definitions:
 An item: an article in a basket, or an attribute-value pair
 A transaction: items purchased in a basket; it may have TID
(transaction ID)
 A transactional dataset: A set of transactions
Example: Market basket Analysis:
Basket1: {bread, cheese, milk}
Basket2: {apple, eggs, salt, yogurt}
…
Basketn: {biscuit, eggs, milk}
An itemset is a set of items.
 E.g., {milk, bread, cereal} is an itemset.
A k-itemset is an itemset with k items.
April 29, 2017
Data Mining: Concepts and Techniques
15
Basic Concepts





The occurrence frequency of an itemset : is the number of
transactions that contain the itemset. This is also known, simply, as
the frequency or support count of the itemset.
Support of an itemset: Percentage of transactions which contain
that itemset.
An itemset satises minimum support if the occurrence frequency of
the itemset is greater than or equal to the product of min_sup and
the total number of transactions in D.
If an itemset satises minimum support, then it is a frequent
itemset. (Frequent itemset: Itemset whose number of
occurrences is above a threshold).
The set of frequent k-itemsets is commonly denoted by Lk
April 29, 2017
Data Mining: Concepts and Techniques
16
Association Rule Mining Problem



Given a set of items I={I1,I2,…,Im} and a
database of transactions D={t1,t2, …, tn}
where ti={Ii1,Ii2, …, Iik} and Iij  I,
The Association Rule Mining Problem is
to identify all association rules X  Y with a
minimum support and confidence.
Rules that satisfy both a minimum support
threshold (min_sup) and a minimum
confidence threshold (min_conf) are called
strong.
April 29, 2017
Data Mining: Concepts and Techniques
17
Association Rule Mining Process

Association rule mining is a two-step process:
 Step 1: Find all frequent itemsets. By
definition, each of these itemsets will occur at
least as frequently as a pre-determined
minimum support count.
 Step 2: Generate strong association rules from
the frequent itemsets. By definition, these
rules must satisfy minimum support and
minimum confidence.
April 29, 2017
Data Mining: Concepts and Techniques
18
The Apriori Algorithm






The Apriori algorithm is the most well known association rule
algorithm and used in most commercial products.
The name of the algorithm is based on the fact that the algorithm
uses prior knowledge of frequent itemset properties.
Apriori employs an iterative approach known as a level-wise
search, where k-itemsets are used to explore (k+1)-itemsets.
First, the set of frequent 1-itemsets is found. This set is denoted
L1.
L1 is used to find L2, the frequent 2-itemsets, which is used to find
L3, and so on, until no more frequent k-itemsets can be found.
The finding of each Lk requires one full scan of the database.
April 29, 2017
Data Mining: Concepts and Techniques
19
Apriori property


A frequent (large) itemset is an itemset whose support (S) is ≥
min_sup.
Apriori property (downward closure): any subsets of a frequent
itemset are also frequent itemsets


i.e., if {AB} is a frequent itemset, both {A} and {B} should be a
frequent itemset
Apriori property is used to reduce the search space. It improves the
eficiency of the level-wise generation of frequent itemsets.
ABC
AB
A
April 29, 2017
ABD
AC AD
B
ACD
BC BD
C
BCD
CD
D
Data Mining: Concepts and Techniques
20
Mining Frequent Itemsets: the Key Step


Find the frequent itemsets: the sets of items that have
minimum support
Iteratively:




Find all 1-item frequent itemsets;
then all 2-item frequent itemsets, etc.
In each iteration k, only consider itemsets that contain some
k-1 frequent itemset.
Candidate and frequent itemsets
 C = candidates of size k: those itemsets of size k
k
that could be frequent, given Lk-1
 L = those itemsets that are actually frequent, L 
k
k
Ck (need to scan the database once).
April 29, 2017
Data Mining: Concepts and Techniques
21
The Apriori Algorithm — Example 1

Suppose that the minimum transaction support count
required is 2 (i.e., min_sup = 50%).
TID
100
200
300
400
Items
134
235
1235
25
Database D
April 29, 2017
Data Mining: Concepts and Techniques
22
The Apriori Algorithm — Example 1
Database D
TID
100
200
300
400
itemset sup.
C1
{1}
2
{2}
3
Scan D
{3}
3
{4}
1
{5}
3
Items
134
235
1235
25
C2 itemset sup
L2 itemset sup
{1 3}
{2 3}
{2 5}
{3 5}
join
2
2
3
2
C3 itemset
{2 3 5}
April 29, 2017
prune
{1
{1
{1
{2
{2
{3
Scan D
2}
3}
5}
3}
5}
5}
1
2
1
2
3
2
L1 itemset sup.
prune
{1}
{2}
{3}
{5}
2
3
3
3
C2 itemset
{1 2}
Scan D
{1
{1
{2
{2
{3
3}
5}
3}
5}
5}
join
L3 itemset sup
{2 3 5} 2
Data Mining: Concepts and Techniques
23
The Apriori Algorithm — Example 2

Suppose that the minimum transaction support count required is
2 (i.e., min sup_count = 2, or min_sup = 2/9 = 22%)
Database D
April 29, 2017
Data Mining: Concepts and Techniques
24
The Apriori Algorithm — Example 2
April 29, 2017
Data Mining: Concepts and Techniques
25
Example of Generating Candidates

By convention, Apriopri assumes that items within an itemset are
sorted in lexicograhic order. The join of two k-1 itemsets is
performed by comparing first k-2 items in itemsets.

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}
April 29, 2017
Data Mining: Concepts and Techniques
26
An Example
April 29, 2017
Data Mining: Concepts and Techniques
27
The Apriori Algorithm


Join Step: Ck is generated by joining Lk-1with itself
Prune Step: Any (k-1)-itemset that is not frequent cannot be
a subset of a frequent k-itemset

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;
April 29, 2017
Data Mining: Concepts and Techniques
28
How to Generate Candidates?

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
April 29, 2017
Data Mining: Concepts and Techniques
29
Generating association rules from frequent itemsets


Once the frequent itemsets from transactions in a database D
have been found, it is straightforward to generate strong
association rules from them (where strong association rules
satisfy both minimum support and minimum condence)
confidence(AB)= P(BA) = support_count(AB) / support_count(A)
where support_count(A U B) is the number of transactions containing the
itemsets A U B, and support_count(A) is the number of transactions containing
the itemset A.

Based on this equation, association rules can be generated as
follows.


April 29, 2017
for each frequent itemset l, generate non empty sunbsets of
l.
For every non-empty subset s of l, output the rules s (l-s)
if sup_count(l)/sup_count(s)>=min_conf
Data Mining: Concepts and Techniques
30
Example 6.2 from the Book




the frequent item set [1 2 5]
non empty subsets of l are
 [1 2],[1 5],[2 5],[1],[2],[5]
the resulting association rules are:
 125
conf: 2/4=50%
 152
conf: 2/2=100%
 251
conf: 2/2=100%
 125
conf: 2/6=33%
 215
conf: 2/7=29%
 512
conf: 2/2=100%
if min conf=70%, then 2nd and 3th and last rules are strong
April 29, 2017
Data Mining: Concepts and Techniques
31
How to Count Supports of
Candidates?

Why counting supports of candidates a problem?



The total number of candidates can be very huge
One transaction may contain many candidates
Method:

Candidate itemsets are stored in a hash-tree

Leaf node of hash-tree contains a list of itemsets
and counts


Interior node contains a hash table
Subset function: finds all the candidates contained in
a transaction
April 29, 2017
Data Mining: Concepts and Techniques
32
Methods to Improve Apriori’s Efficiency

Hash-based itemset counting: A k-itemset whose corresponding
hashing bucket count is below the threshold cannot be frequent

Transaction reduction: A transaction that does not contain any
frequent k-itemset is useless in subsequent scans

Partitioning: Any itemset that is potentially frequent in DB must be
frequent in at least one of the partitions of DB

Sampling: mining on a subset of given data, lower support
threshold + a method to determine the completeness

Dynamic itemset counting: add new candidate itemsets only when
all of their subsets are estimated to be frequent
April 29, 2017
Data Mining: Concepts and Techniques
33
Is Apriori Fast Enough? — Performance
Bottlenecks

The core of the Apriori algorithm:



Use frequent (k – 1)-itemsets to generate candidate frequent kitemsets
Use database scan and pattern matching to collect counts for the
candidate itemsets
The bottleneck of Apriori: candidate generation
 Huge candidate sets:



104 frequent 1-itemset will generate 107 candidate 2-itemsets
To discover a frequent pattern of size 100, e.g., {a1, a2, …,
a100}, one needs to generate 2100  1030 candidates.
Multiple scans of database:

April 29, 2017
Needs (n +1 ) scans, n is the length of the longest pattern
Data Mining: Concepts and Techniques
34
Mining Frequent Patterns
Without Candidate Generation

Compress a large database into a compact, FrequentPattern tree (FP-tree) structure



highly condensed, but complete for frequent pattern
mining
avoid costly database scans
Develop an efficient, FP-tree-based frequent pattern
mining method


April 29, 2017
A divide-and-conquer methodology: decompose mining
tasks into smaller ones
Avoid candidate generation: sub-database test only!
Data Mining: Concepts and Techniques
35
Construct FP-tree from a
Transaction DB
TID
100
200
300
400
500
Items bought
(ordered) frequent items
{f, a, c, d, g, i, m, p}
{f, c, a, m, p}
{a, b, c, f, l, m, o}
{f, c, a, b, m}
{b, f, h, j, o}
{f, b}
{b, c, k, s, p}
{c, b, p}
{a, f, c, e, l, p, m, n}
{f, c, a, m, p}
Steps:
{}
Header Table
1. Scan DB once, find frequent
1-itemset (single item
pattern)
2. Order frequent items in
frequency descending order
3. Scan DB again, construct
FP-tree
April 29, 2017
min_support = 0.5
Item frequency head
f
4
c
4
a
3
b
3
m
3
p
3
Data Mining: Concepts and Techniques
f:4
c:3
c:1
b:1
a:3
b:1
p:1
m:2
b:1
p:2
m:1
36
Benefits of the FP-tree Structure


Completeness:
 never breaks a long pattern of any transaction
 preserves complete information for frequent pattern
mining
Compactness
 reduce irrelevant information—infrequent items are gone
 frequency descending ordering: more frequent items are
more likely to be shared
 never be larger than the original database (if not count
node-links and counts)
 Example: For Connect-4 DB, compression ratio could be
over 100
April 29, 2017
Data Mining: Concepts and Techniques
37
Mining Frequent Patterns Using FPtree


General idea (divide-and-conquer)
 Recursively grow frequent pattern path using the FPtree
Method
 For each 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)
April 29, 2017
Data Mining: Concepts and Techniques
38
Major Steps to Mine FP-tree
1)
Construct conditional pattern base for each node in the
FP-tree
2)
Construct conditional FP-tree from each conditional
pattern-base
3)
Recursively mine conditional FP-trees and grow
frequent patterns obtained so far

April 29, 2017
If the conditional FP-tree contains a single path,
simply enumerate all the patterns
Data Mining: Concepts and Techniques
39
Step 1: From FP-tree to Conditional
Pattern Base



Starting at the frequent header table in the FP-tree
Traverse the FP-tree by following the link of each frequent item
Accumulate all of transformed prefix paths of that item to form a
conditional pattern base (e.g. Having item x at the end of list L)
Header Table
Item frequency head
f
4
c
4
a
3
b
3
m
3
p
3
April 29, 2017
{}
Conditional pattern bases
f:4
c:3
c:1
b:1
a:3
b:1
p:1
item
cond. pattern base
c
f:3
a
fc:3
b
fca:1, f:1, c:1
m:2
b:1
m
fca:2, fcab:1
p:2
m:1
p
fcam:2, cb:1
Data Mining: Concepts and Techniques
40
Properties of FP-tree for Conditional
Pattern Base Construction

Node-link property


For any frequent item ai, all the possible frequent
patterns that contain ai can be obtained by following
ai's node-links, starting from ai's head in the FP-tree
header
Prefix path property

April 29, 2017
To calculate the frequent patterns for a node ai in a
path P, only the prefix sub-path of ai in P need to be
accumulated, and its frequency count should carry the
same count as node ai.
Data Mining: Concepts and Techniques
41
Mining Frequent Patterns by
Creating Conditional Pattern-Bases
Item
Conditional pattern-base
Conditional FP-tree
p
{(fcam:2), (cb:1)}
{(c:3)}|p
m
{(fca:2), (fcab:1)}
{(f:3, c:3, a:3)}|m
b
{(fca:1), (f:1), (c:1)}
Empty
a
{(fc:3)}
{(f:3, c:3)}|a
c
{(f:3)}
{(f:3)}|c
f
Empty
Empty
April 29, 2017
Data Mining: Concepts and Techniques
42
Step 2: Construct Conditional FP-tree

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
Header Table
Item frequency head
f
4
c
4
a
3
b
3
m
3
p
3
{}
f:4
c:3
c:1
b:1
a:3
b:1
p:1
m-conditional pattern
base:
fca:2, fcab:1
{}

f:3 
m:2
b:1
c:3
p:2
m:1
a:3
All frequent patterns
concerning m
m,
fm, cm, am,
fcm, fam, cam,
fcam
m-conditional FP-tree
April 29, 2017
Data Mining: Concepts and Techniques
43
Step 3: Recursively mine the
conditional FP-tree
{}
{}
Cond. pattern base of “am”: (fc:3)
f:3
c:3
f:3
am-conditional FP-tree
c:3
{}
Cond. pattern base of “cm”: (f:3)
a:3
f:3
m-conditional FP-tree
cm-conditional FP-tree
{}
Cond. pattern base of “cam”: (f:3)
f:3
cam-conditional FP-tree
April 29, 2017
Data Mining: Concepts and Techniques
44
Single FP-tree Path Generation


Suppose an FP-tree T has a single path P
The complete set of frequent pattern of T can be
generated by enumeration of all the combinations of the
sub-paths of P
{}
f:3
c:3

a:3
All frequent patterns
concerning m
m,
fm, cm, am,
fcm, fam, cam,
fcam
m-conditional FP-tree
April 29, 2017
Data Mining: Concepts and Techniques
45
Principles of Frequent Pattern
Growth

Pattern growth property


Let  be a frequent itemset in DB, B be 's
conditional pattern base, and  be an itemset in B.
Then    is a frequent itemset in DB iff  is
frequent in B.
“abcdef ” is a frequent pattern, if and only if


April 29, 2017
“abcde ” is a frequent pattern, and
“f ” is frequent in the set of transactions containing
“abcde ”
Data Mining: Concepts and Techniques
46
Why Is Frequent Pattern Growth
Fast?

Our performance study shows

FP-growth is an order of magnitude faster than
Apriori, and is also faster than tree-projection

Reasoning

No candidate generation, no candidate test

Use compact data structure

Eliminate repeated database scan

Basic operation is counting and FP-tree building
April 29, 2017
Data Mining: Concepts and Techniques
47
FP-growth vs. Apriori: Scalability With
the Support Threshold
Data set T25I20D10K
100
D1 FP-grow th runtime
90
D1 Apriori runtime
80
Run time(sec.)
70
60
50
40
30
20
10
0
0
April 29, 2017
0.5
1
1.5
2
Support threshold(%)
Data Mining: Concepts and Techniques
2.5
3
48
FP-growth vs. Tree-Projection: Scalability
with Support Threshold
Data set T25I20D100K
140
D2 FP-growth
Runtime (sec.)
120
D2 TreeProjection
100
80
60
40
20
0
0
April 29, 2017
0.5
1
Support threshold (%)
Data Mining: Concepts and Techniques
1.5
2
49
Presentation of Association Rules
(Table Form )
April 29, 2017
Data Mining: Concepts and Techniques
50
Visualization of Association Rule Using Plane Graph
April 29, 2017
Data Mining: Concepts and Techniques
51
Visualization of Association Rule Using Rule Graph
April 29, 2017
Data Mining: Concepts and Techniques
52
Chapter 6: Mining Frequent Patterns, Association
and Correlations: Basic Concepts and Methods
 Basic Concepts
 Frequent Itemset Mining Methods
 Which Patterns Are Interesting?—Pattern
Evaluation Methods
 Summary
53
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
P( A B)
lift 
P( A) P( B)
lift ( B, C ) 
2000 / 5000
 0.89
3000 / 5000 * 3750 / 5000
lift ( B, C ) 
Basketball
Not basketball
Sum (row)
Cereal
2000
1750
3750
Not cereal
1000
250
1250
Sum(col.)
3000
2000
5000
1000 / 5000
 1.33
3000 / 5000 *1250 / 5000
54
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?
55
Null-Invariant Measures
56
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

Null-transactions
w.r.t. m and c
April 29, 2017
Kulczynski
measure (1927)
Data Mining: Concepts and Techniques
Null-invariant
Subtle: They disagree57
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
58
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
Summary

Association rule mining


probably the most significant contribution from the
database community in KDD
A large number of papers have been published

Many interesting issues have been explored

An interesting research direction

April 29, 2017
Association analysis in other types of data: spatial
data, multimedia data, time series data, etc.
Data Mining: Concepts and Techniques
60
Summary


Basic concepts: association rules, supportconfident 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
61
References










R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent
itemsets. In Journal of Parallel and Distributed Computing (Special Issue on High Performance Data
Mining), 2000.
R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large
databases. SIGMOD'93, 207-216, Washington, D.C.
R. Agrawal and R. Srikant. Fast algorithms for mining association rules. VLDB'94 487-499, Santiago,
Chile.
R. Agrawal and R. Srikant. Mining sequential patterns. ICDE'95, 3-14, Taipei, Taiwan.
R. J. Bayardo. Efficiently mining long patterns from databases. SIGMOD'98, 85-93, Seattle,
Washington.
S. Brin, R. Motwani, and C. Silverstein. Beyond market basket: Generalizing association rules to
correlations. SIGMOD'97, 265-276, Tucson, Arizona.
S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for
market basket analysis. SIGMOD'97, 255-264, Tucson, Arizona, May 1997.
K. Beyer and R. Ramakrishnan. Bottom-up computation of sparse and iceberg cubes. SIGMOD'99, 359370, Philadelphia, PA, June 1999.
D.W. Cheung, J. Han, V. Ng, and C.Y. Wong. Maintenance of discovered association rules in large
databases: An incremental updating technique. ICDE'96, 106-114, New Orleans, LA.
M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, and J. D. Ullman. Computing iceberg queries
efficiently. VLDB'98, 299-310, New York, NY, Aug. 1998.
April 29, 2017
Data Mining: Concepts and Techniques
62
References (2)










G. Grahne, L. Lakshmanan, and X. Wang. Efficient mining of constrained correlated sets. ICDE'00, 512521, San Diego, CA, Feb. 2000.
Y. Fu and J. Han. Meta-rule-guided mining of association rules in relational databases. KDOOD'95,
39-46, Singapore, Dec. 1995.
T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Data mining using two-dimensional optimized
association rules: Scheme, algorithms, and visualization. SIGMOD'96, 13-23, Montreal, Canada.
E.-H. Han, G. Karypis, and V. Kumar. Scalable parallel data mining for association rules. SIGMOD'97,
277-288, Tucson, Arizona.
J. Han, G. Dong, and Y. Yin. Efficient mining of partial periodic patterns in time series database.
ICDE'99, Sydney, Australia.
J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. VLDB'95, 420-431,
Zurich, Switzerland.
J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. SIGMOD'00, 1-12,
Dallas, TX, May 2000.
T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of ACM,
39:58-64, 1996.
M. Kamber, J. Han, and J. Y. Chiang. Metarule-guided mining of multi-dimensional association rules
using data cubes. KDD'97, 207-210, Newport Beach, California.
M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules
from large sets of discovered association rules. CIKM'94, 401-408, Gaithersburg, Maryland.
April 29, 2017
Data Mining: Concepts and Techniques
63
References (3)









F. Korn, A. Labrinidis, Y. Kotidis, and C. Faloutsos. Ratio rules: A new paradigm for fast, quantifiable
data mining. VLDB'98, 582-593, New York, NY.
B. Lent, A. Swami, and J. Widom. Clustering association rules. ICDE'97, 220-231, Birmingham,
England.
H. Lu, J. Han, and L. Feng. Stock movement and n-dimensional inter-transaction association rules.
SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD'98), 12:112:7, Seattle, Washington.
H. Mannila, H. Toivonen, and A. I. Verkamo. Efficient algorithms for discovering association rules.
KDD'94, 181-192, Seattle, WA, July 1994.
H. Mannila, H Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. Data
Mining and Knowledge Discovery, 1:259-289, 1997.
R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining association rules. VLDB'96, 122133, Bombay, India.
R.J. Miller and Y. Yang. Association rules over interval data. SIGMOD'97, 452-461, Tucson, Arizona.
R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of
constrained associations rules. SIGMOD'98, 13-24, Seattle, Washington.
N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association
rules. ICDT'99, 398-416, Jerusalem, Israel, Jan. 1999.
April 29, 2017
Data Mining: Concepts and Techniques
64
References (4)










J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules.
SIGMOD'95, 175-186, San Jose, CA, May 1995.
J. Pei, J. Han, and R. Mao. CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets.
DMKD'00, Dallas, TX, 11-20, May 2000.
J. Pei and J. Han. Can We Push More Constraints into Frequent Pattern Mining? KDD'00. Boston,
MA. Aug. 2000.
G. Piatetsky-Shapiro. Discovery, analysis, and presentation of strong rules. In G. Piatetsky-Shapiro
and W. J. Frawley, editors, Knowledge Discovery in Databases, 229-238. AAAI/MIT Press, 1991.
B. Ozden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. ICDE'98, 412-421, Orlando,
FL.
J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules.
SIGMOD'95, 175-186, San Jose, CA.
S. Ramaswamy, S. Mahajan, and A. Silberschatz. On the discovery of interesting patterns in
association rules. VLDB'98, 368-379, New York, NY..
S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule mining with relational database
systems: Alternatives and implications. SIGMOD'98, 343-354, Seattle, WA.
A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in
large databases. VLDB'95, 432-443, Zurich, Switzerland.
A. Savasere, E. Omiecinski, and S. Navathe. Mining for strong negative associations in a large
database of customer transactions. ICDE'98, 494-502, Orlando, FL, Feb. 1998.
April 29, 2017
Data Mining: Concepts and Techniques
65
References (5)










C. Silverstein, S. Brin, R. Motwani, and J. Ullman. Scalable techniques for mining causal
structures. VLDB'98, 594-605, New York, NY.
R. Srikant and R. Agrawal. Mining generalized association rules. VLDB'95, 407-419, Zurich,
Switzerland, Sept. 1995.
R. Srikant and R. Agrawal. Mining quantitative association rules in large relational tables.
SIGMOD'96, 1-12, Montreal, Canada.
R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. KDD'97, 67-73,
Newport Beach, California.
H. Toivonen. Sampling large databases for association rules. VLDB'96, 134-145, Bombay, India,
Sept. 1996.
D. Tsur, J. D. Ullman, S. Abitboul, C. Clifton, R. Motwani, and S. Nestorov. Query flocks: A
generalization of association-rule mining. SIGMOD'98, 1-12, Seattle, Washington.
K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Computing optimized
rectilinear regions for association rules. KDD'97, 96-103, Newport Beach, CA, Aug. 1997.
M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. Parallel algorithm for discovery of
association rules. Data Mining and Knowledge Discovery, 1:343-374, 1997.
M. Zaki. Generating Non-Redundant Association Rules. KDD'00. Boston, MA. Aug. 2000.
O. R. Zaiane, J. Han, and H. Zhu. Mining Recurrent Items in Multimedia with Progressive
Resolution Refinement. ICDE'00, 461-470, San Diego, CA, Feb. 2000.
April 29, 2017
Data Mining: Concepts and Techniques
66
http://www.cs.sfu.ca/~han/dmbook
Thank you !!!
April 29, 2017
Data Mining: Concepts and Techniques
67