Download Mining Indirect Associations in Web Data

Discovery of Indirect Association from Web Usage Data
Vipin Kumar
Army High Performance Computing Research Center
Department of Computer Science & Engineering
University of Minnesota
[email protected]
Homepage: http://www.cs.umn.edu/~kumar
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Overview
Application/
Web Server
Web
User
Internet
Knowledge
© Vipin Kumar, PAKDD (May 2002)
Web Usage
Mining
University of Minnesota / Army High Performance Research Center
Web
usage
data
‹#›
Overview…

Web Usage Mining:
– the automatic extraction of non-trivial patterns from
Web usage data

Main techniques:
– Clustering:
 to find groups of users who share similar browsing behavior
– Classification:
 to categorize Web users according to their past access
history
– Association:
 to determine what are the set of page views often accessed
together in the same server session
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Application of Web Usage Mining
Web Usage
Mining
Personalization
Site H elper
Letizia
Web Watcher
M obasher
Analog
Krishnapuram
System
Improvement
R exford
Schecter
Ag g arwal
Site
M odification
Adaptive Sites
WebSIFT
WU M
SpeedTracer
WebLog M iner
Shahabi
Business
Intellig ence
SurfAid
Buchner
Tuzhilin
U sag e
C haracterization
Pitkow
Arlitt
M anley
Almeida
Source: J. Srivastava, R. Cooley, M. Deshpande, PN Tan, Web Usage Mining: Discovery
and Applications of Usage Patterns from Web Data, SIGKDD Explorations (2000)
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Taxonomy of Web Usage Mining
Pro ject
Ap p licatio n
Data
Fo cu s
Serv er
W eb SIFT (C TS9 9 )
Gen eral
x
Sp eed Tracer (W YB 9 8 ,C PY9 6 ) Gen eral
x
W UM (SF9 8 )
Gen eral
x
Sh ah ab i (SZAS9 7 ,ZASS9 7 )
Gen eral
Site Help er (NW 9 7 )
Perso n alizatio n
x
Letizia (Lie9 5 )
Perso n alizatio n
W eb W atch er (JFM9 7 )
Perso n alizatio n
Krish n ap u ram(NKJ9 9 )
Perso n alizatio n
x
An alo g (YJGD9 6 )
Perso n alizatio n
x
Mo b ash er (MC S9 9 )
Perso n alizatio n
x
Tu zh ilin (PT9 8 )
B u sin ess
x
Su rfAid
B u sin ess
x
B u ch n er(B M9 8 )
B u sin ess
x
W eb Tren d s,Hitlist,Accru e,etc. B u sin ess
x
W eb Lo g Min er (ZXH9 8 )
B u sin ess
x
Pag eGath er,SC ML (PE9 8 ,PE9 9 )Site Mo d ificatio n
x
Man ley (Man 9 7 )
C h aracterizatio n
x
Arlitt(AW 9 6 )
C h aracterizatio n
x
Pitk o w(PIT9 7 ,PIT9 8 )
C h aracterizatio n
x
Almeid a(AB C 9 6 )
C h aracterizatio n
x
R ex fo rd (C KR 9 8 )
Sy stem Imp ro v e.
x
Sch ech ter(SKS9 8 )
Sy stem Imp ro v e.
x
Ag g arwal(AY9 7 )
Sy stem Imp ro v e.
So u rce
Pro x y C lien t
x
x
x
x
x
x
Data
Ty p e
User
Site
Stru ctu re C o n ten t Usag e Pro file Sin g le Mu lti Sin g le Mu lti
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Source: J. Srivastava, R. Cooley, M. Deshpande, PN Tan, Web Usage Mining: Discovery
and Applications of Usage Patterns from Web Data, SIGKDD Explorations (2000)
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Mining Association Patterns in Web Data
Input
Output
/home  /product  /product/electronics
Frequent
Itemsets
/home  /shipping  /shipping/status
Association
Rules
/home  /product  /product/offer
 /product/electronics/tv
/home  /product  /product/electronics
 /product/electronics/tv
Click-streams
© Vipin Kumar, PAKDD (May 2002)
Association
Mining
University of Minnesota / Army High Performance Research Center
Sequential
Patterns
Association
Patterns
‹#›
Mining Association Patterns in Web Data…

Provide the following information:
– What are the set of pages frequently accessed
together by Web users? (frequent itemsets)
– What page will be fetched next? (association rules)
– What are the paths frequently traversed by Web
users? (sequential patterns)

Web association patterns are useful:
–
–
–
–
To improve Web site design
To develop prefetching and Web caching policies.
To recommend related pages
To collect business intelligence about behavior of
Web users
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Association Mining is Support-based

Frequent itemset:
– combination of pages that have support greater than
a user-specified minimum threshold

Association Rule:
s,c
Support, s 
A B


Number of sessions that contain A and B
Total number of sessions
Confidence , c 
Number of sessions that contain A and B
Number of sessions that contain A
Higher support implies greater statistical significance
Support-based pruning constraints the exponential
complexity and makes the association rule computation
tractable
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Can Infrequent Patterns be Interesting?

If support between A and B is too small, then there
may be a negative correlation between A and B
– P(A,B) < P(A) P(B)
– If (A  B) has a high support, then support(A,B) will tend
to be low because:
 P(A B) = P(B) – P(A,B)
– Example: Coffee  Tea
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Approach 1: Using Negative Items


Session Id
A
A
B
B
C
C
D
D
1
1
0
0
1
1
0
0
1
2
1
0
0
1
0
1
0
1
3
1
0
0
1
1
0
0
1
4
1
0
1
0
0
1
0
1
5
1
0
0
1
0
1
1
0
Computationally expensive
Tends to produce too many negative associations
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Approach 2: Negative Itemsets

Savasere et al [1998]:
– A negative itemset is a set of items whose actual
support is significantly lower than its expected support
– Expected support can be computed using item
taxonomy
Suppose C and G are frequent:
A
F
C
B
D
G
E
© Vipin Kumar, PAKDD (May 2002)
J
H
K
sup( CG)  sup( E )  sup( J )
sup( C )  sup( G )
sup( CG)  sup( J )
Exp(sup( CJ )) 
sup( G )
sup( CG)  sup( H )
Exp(sup( CH )) 
sup( G )
Exp(sup( EJ )) 
University of Minnesota / Army High Performance Research Center
‹#›
Indirect Association
a
IF:
THEN:
M
b
a and M are frequent and highly dependent on each other
b and M are frequent and highly dependent on each other
a and b are expected to be frequent
If a and b are infrequent, it has an interesting negative association
 a and b are indirectly associated via mediator M
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Non-Sequential Indirect Association: Formulation
M
a

b
A pair of Web pages, a and b, are indirectly
associated via mediator set M, if :
– Sup({a,b}) < ts
– Sup({a}  M)  tf, Sup({b}  M)  tf
– Dep(a,M)  td, Dep(b,M)  td

where Dep(x,M) can be any “reasonable”
objective measures
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Finding Interesting Negative Associations
For all pairs of items:
With
Mediator
Frequent
FM
No
Mediator
FN
Minimum itempair support
Infrequent
IM
IN
If
IM/FM = IN/FN
mediator thresholds
© Vipin Kumar, PAKDD (May 2002)
then Indirect Association
is not surprising
University of Minnesota / Army High Performance Research Center
‹#›
Finding Interesting Negative Associations
With
Mediator
No
Mediator
FM
FN
Frequent
• IM/FM is small
• IM/IN is small
 Indirect Association
Infrequent
IN
is interesting
IM
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Finding Interesting Negative Association
1
10
Indirect Association is
interesting when
minimum itempair
support threshold is
small.
IM/FM
IM/IN
0
10
But, if threshold is too
low, very few indirect
associations are
obtained.
-1
10
-2
10
1
3
5
7
9
11 13 15 17 19 21
Minimum itempair support (ts )
© Vipin Kumar, PAKDD (May 2002)
23
25
27
29
University of Minnesota / Army High Performance Research Center
‹#›
Application: Market-Basket Analysis
Substitute items: up-sell
– Pavilion PC  17” Monitor  Pavilion multimedia PC
Competing items: competitive analysis
– Coke  Ruffles  Pepsi
Complementary items: cross-sell
– Tekken 3  Playstation Memory Card  Tomb Raider 2
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Other Applications: Information Retrieval
Identify synonyms and antonyms
Identify the different contexts of a queried word
– Useful to group together query results
Data
Mining
Web
Trade
Worker
© Vipin Kumar, PAKDD (May 2002)
Union
Gold
Soviet
University of Minnesota / Army High Performance Research Center
‹#›
Other Applications: Stock Market
Dow Jones (DJI)
% Change
5
0
Let v(t): value of time series on day t
% change (t) = (v(t) - v(t-1))/v(t-1)*100
-5
-10
11/8/99
Microsoft Corp (MSFT)
3/28/02
Event definition:
Up: % change > 2%
Down: % change < -2%
% Change
20
0
-20
11/8/99
RedHat Inc (RHAT)
3/28/02
% Change
20
Support counts:
{DJI-Down,MSFT-Down} : 26
{RHAT-Up,MSFT-Down} : 24
{DJI-Down,RHAT-Up}
:4
0
-20
11/8/99
3/28/02
Indirect Association can partition
events that are associated with the
movement of a stock price.
MSFTDown
DJI-Down
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
RHAT-Up
‹#›
Sequential Indirect Association: Formulation

A pair of Web pages, a and b, forms a
sequential indirect association via a mediator
sequence, w, if the following conditions are
satisfied:
– Support({a,b}) < ts
– Support(s1)  tf, Support(s2)  tf
– Dependence(a,w)  td,
Dependence(b,w)  td
– w does not contain a and b

(s1=aw or wa, s1=bw or wb)
discover groups of users who have different
interests but share a common traversal path
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Types of Sequential Indirect Association
Different entry points
a
Different entry and user interests
w
b
w
a
a
b
w
Different user interests
Type C (convergence)
© Vipin Kumar, PAKDD (May 2002)
Type D (divergence)
b
Type T (transitive)
University of Minnesota / Army High Performance Research Center
‹#›
Clustering vs Indirect Association

Clustering is another way to discover different groups of
users
–
A. Banerjee and J.Ghosh, Clickstream Clustering using Weighted Longest Common
Subsequences, Workshop on Web Mining (2001)
–
TW Yan, M Jacobsen, H Garcia-Molina and U Dayal, From User Access Patterns to Dynamic
Hypertext Linking, Proc of the 5th International World Wide Web Conference (1996).
–
YJ Fu, K Sandhu, MY Shih, A Generalization-Based Approach to Clustering of Web Usage
Sessions, Web Usage Analysis and User Profiling, LNAI (2001)


Clustering cannot find distinct groups of users who share
a similar traversal path since the support of the mediator is
large (mediator often contains navigation pages)
It is more likely that several indirect associations are
contained within a single cluster, rather than each indirect
association connects between two separate clusters
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Indirect Association for Web Data
Site Structure
A
C
Large 3-itemset
(minsup = 40%)
Pattern
{A,B,D}
{A,B,E}
{B,C,D}
B
Support
2
2
2
{A,B}
D
E
D
E
Web sessions
Session Id
1
2
3
4
5
Non-sequential
Indirect Association
Sequence
<A,B,C,B,D>
<A,B,E>
<B,C>
<A,B,E,B,D>
<B,D,B,C>
© Vipin Kumar, PAKDD (May 2002)
Sequential Indirect
Association
Large 3-sequence
(minsup = 40%)
Pattern
A->B->D
A->B->E
A
Support
2
2
B
D
University of Minnesota / Army High Performance Research Center
E
‹#›
Impact of Site Structure on Associations

Navigation pages (e.g. home pages and hub pages)
tend to have higher support than content pages
5
5
10
10
4
4
10
10
3
3
10
Support
Support
10
2
10
1
1
10
10
0
10
2
10
0
1
2
3
4
5
6
7
8
Number of hops away from the home page
9
10
10
1
2
3
4
5
6
7
Number of outgoing links
8
9
10
Source: U of Minnesota Computer Science department Web logs (Jan 1-31, 2001)


If threshold is too high, most of the patterns contain
only navigation pages
If threshold is too low, too many patterns!
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
How Indirect Association helps

Indirect association groups together patterns that
have similar substructures
– The common substructures (mediators) often contain
the navigation pages
Mediator
A
Navigation pages
B
D
© Vipin Kumar, PAKDD (May 2002)
E
University of Minnesota / Army High Performance Research Center
‹#›
Grouping Indirect Associations

Indirect associations can also be grouped
together into more compact structures if they
share a common mediator
A,B
A,B
D
E
D
F
E
A,B
Check degree of association
D
F
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Viewer for Non-Sequential Indirect Association
Indirect
Pairs
(dashed
line)
List of
Mediators
Frequent
Pairs (solid
line)
Currently
Selected
Mediator
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Experimental Setup

Data sources:
– UMN: University of Minnesota Web log

contains 91,443 page views and 34,526 sessions
– EC: E-commerce Web log


Contains 6664 page views and 143,604 sessions
Steps:
1. Preprocessing
– identify sessions, convert sessions into “transactions”
2. Extract frequent itemsets or sequences:
– apply Apriori algorithm to find frequent itemsets
– Apply GSP algorithm to find frequent sequences
3. Apply indirect association algorithm
4. Merge indirect associations that have common
mediator
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Experimental Results (UMN)
Prospective
graduate
students
Contact
information
Students taking CS
as minor subject
CS Graduate
student association
Students planning to
take PhD exam
Experimental Results (UMN)
Regular Seminar/
Colloquium
attendees
Contact information
Students taking CS as minor subject
Experimental Results (EC)
Experimental Results (EC)
Experimental Results (EC)
Experimental Results (EC)
Experimental Results (EC)
Experimental Results (EC)
#
a
Mediator, w
b
1
Stereo
HomeElectronicsAuto
Radar Detector
2
Hunting
HomeSporting Goods
Aerobics
3
Women’s gown set
HomeApparel
Men’s boots
4
Camcorders
HomeElectronicsVideo
13” TV and VCR combo
5
Scanner
HomeElectronicsComputer
Multimedia Computer
6
Shower curtains
DomesticsBath Shop
Towel set
7
Bedroom furniture frame
Home & Accessories  Furniture
Oak nightstand
8
Speakers
ElectronicsStereo
CD boombox
9
Men’s
HomeJewelry
Ladies
10
Cordless phone
HomeTelephones
Answering device
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
Conclusion

Indirect Association provides an alternative
approach to capture interesting infrequent
patterns
– For Web data, indirect association represents the
distinct interests of Web users who share similar
traversal path
– Such patterns cannot be easily found using standard
association and clustering techniques

Indirect Association can be used to group
together patterns into more compact structures
– Navigation pages form the mediators
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›
References
1.
PN Tan, V Kumar, J Srivastava, Indirect Association : Mining
Higher Order Dependencies in Data, In Proc of the 4th European
Conference on Principles and Practice of Knowledge Discovery in
Databases, Lyon, France, Sept 13-16, 632-637 (2000)
2.
PN Tan, V Kumar, H Kuno, Using SAS for Mining Indirect
Associations in Data, In Proc of the Western Users of SAS
Software Conference (2001)
3.
PN Tan, V Kumar, Mining Indirect Associations in Web Data, In
Proc of WebKDD 2001: Mining Log Data Across All Customer
TouchPoints, August (2001)
4.
KDD Web Mining workshops: WebKDD 1999, WebKDD 2000,
WebKDD 2001
5.
SIAM Workshop on Web Mining 2001 and 2002
© Vipin Kumar, PAKDD (May 2002)
University of Minnesota / Army High Performance Research Center
‹#›