1. Discovery of Significant Usage Patterns from Clusters of Clickstream Data
Lin Lu, Margaret Dunham, and Yu Meng
Department of Computer Science and Engineering
Southern Methodist University
Dallas, Texas
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2. Beginning/ending Web page(s)
Introduction
Significant Usage Patterns (SUP)
- SUP is extracted from clusters of abstracted user
sessions
- Use a unique two-phase abstraction technique
- With desired beginning and/or ending Web pages
- With normalized probability
Clustering
Abstraction
Beginning/ending Web page(s)
Normalized
Sequential Pattern N Y* -
Maximal Frequent Sequence
Maximal Frequent Forward Sequence
User Preferred Navigational Trail [1,2]
Significant Usage Pattern Y
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3. Model WebKDD’05 3 Sessionized Web Log Abstraction Hierarchy
Sub-abstract URLs
Sub-Abstracted Sessions
Sub-Abstracted Sessions
Apply Needleman-Wunsch global alignment algorithm
Similarity Matrix
Apply Nearest neighbor clustering algorithm
Clusters of User Sessions
Abstraction Hierarchy
Concept-based Abstracted URLs
Concept-based Abstracted Sessions per Cluster
Build Markov model for each cluster
Transition Matrix per Cluster
Pattern Discovery
SUPs per Cluster
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4. Alignment of Web sessions
Create sub-abstracted Web sessions
URL -> {<Concept hierarchy keyword> <Unique ID> <|>}
JCPenney Homepage D1 … Dn C1 Cn I1 In
Department level
Category level
Item level
Fig 1. Hierarchy of J.C. Penney Web site D2
Example:
D0|C875|I D0|C875|I P P P
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5. Alignment of Web sessions
Computing the similarity between any two Web pages
The higher the level in the hierarchy, the more importance in determining the similarity of two Web pages, should give more weight.
Scoring scheme
- step 1: determine the longer page representation
string in the two Web page representations.
- step 2: weight is assigned to each level in the hierarchy: the
lowest level in longer page representation string is
given weight 2 to its abstract level, the second to the
lowest level is given weight 4 to its abstract level, and
so on. The corresponding ID is always given weight 1.
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6. Alignment of Web sessions
Computing the similarity between any two Web pages
- step 1: compare the two Web page representation strings
from the left to the right and stopped at the first
pair which they are different.
- step 2: compute the ratio of the sum of the weights of those
matching parts to the weight of longer page
representation string.
Example:
Page 1: D0|C875|I Weight= =14
Page 2: D0|C875 Weight= =12
Similarity=12/14=0.857
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7. Model WebKDD’05 7 Sessionized Web Log Abstraction Hierarchy
Sub-abstract URLs
Sub-Abstracted Sessions
Apply Needleman-Wunsch global alignment algorithm
Similarity Matrix
Apply Nearest neighbor clustering algorithm
Clusters of User Sessions
Abstraction Hierarchy
Concept-based Abstracted URLs
Concept-based Abstracted Sessions per Cluster
Build Markov model for each cluster
Transition Matrix per Cluster
Pattern Discovery
Patterns per Cluster
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8. Alignment of Web sessions
Computing optimal alignment of two sequences
using Needleman-Wunsch algorithm Y1 …
Yj-1 Yj Yn -d
-(j-1)d
-jd
-nd X1
Xi-1
-(i-1)d Xi
-id Xm
-md
A(m, n)
A(i-1, j-1)
A(i-1, j)
A(i, j-1)
A(i, j)
A(i, j) = max[A(i-1, j-1)+s(Xi, Yj); A(i-1, j)-d; A(i, j-1)-d]
where s(Xi, Yj) is the similarity between Xi and Yj,
d is the score of aligning Xi (Yj) with a gap
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9. Alignment of Web sessions
Apply Needleman-Wunsch global alignment algorithm
Scoring scheme [3]
if (matching) score = 20; //a pair of Web pages with similarity 1
else if (mis-matching) score = –10; //a pair of Web pages with similarity 0
else if (gap) score = –10; //a Web page aligns with a gap
else score = –10 ~ 20; //the pair of Web pages with similarity between 0 and 1
Example:
P D0|C0|I D469|C469 D2652|C2652
D469|C16758|I D0|C0|I D469|C469
P47104
D0|C0|I
D469|C469
D2652|C2652
-10
-20
-30
-40
D469|C16758|I
5.7
-4.3
-14.3 10
17.1
7.1 30
32.1
Thus, session similarity = 32.1/4 = 8.025
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10. Model WebKDD’05 10 Sessionized Web Log Abstraction Hierarchy
Sub-abstract URLs
Sub-Abstracted Sessions
Apply Needleman-Wunsch global alignment algorithm
Similarity Matrix
Apply Nearest neighbor clustering algorithm
Clusters of User Sessions
Abstraction Hierarchy
Concept-based Abstracted URLs
Concept-based Abstracted Sessions per Cluster
Build Markov model for each cluster
Transition Matrix per Cluster
Pattern Discovery
Patterns per Cluster
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11. Model WebKDD’05 11 Sessionized Web Log Abstraction Hierarchy
Sub-abstract URLs
Sub-Abstracted Sessions
Apply Needleman-Wunsch global alignment algorithm
Similarity Matrix
Apply Nearest neighbor clustering algorithm
Clusters of User Sessions
Abstraction Hierarchy
Concept-based Abstracted URLs
Concept-based Abstracted Sessions per Cluster
Build Markov model for each cluster
Transition Matrix per Cluster
Pattern Discovery
Patterns per Cluster
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12. Create Concept-based Abstracted Sessions
Represent the abstracted page accesses in a session as a sequence like: P1 D1 C1 I1 P2 D2 C2 I2 …
In a session, the same Pi, Di, Ci, and Ii (i=1, 2…) represents the same page. However, in different sessions, the same page may be represented by different elements.
Example:
Original session: D7107|C7121 D7107|C7126|I076bdf3
D7107|C7131|I084fc96 D7107|C7131
P55730 P96 P27 P14 P27592 P28 P33711
Abstracted session: C1 I1 I2 C2 P1 P2 P3 P4 P5 P6 P
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13. Generating Significant Usage Patterns
Use Markov model to represent sessions in each cluster
Example:
0.4
0.17
0.2
0.5
0.33
0.25
0.75 1 S 2 5 3 4 E
(1) 1, 2, 3, 5, 4
(2) 2, 4, 3, 5
(3) 3, 2, 4, 5
(4) 1, 3, 4, 3
(5) 4, 2, 3, 4, 5
The probability of a path
normalized
where Pti is the transition probability between two adjacent states
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14. Generating Significant Usage Patterns
Example:
 > 0.4, end state is 4
 > 0.4, beginning state is 1, end state is 4
SUP 
S1234
0.45
1234
0.46
S12354
0.53
12354
0.56
S124
124
0.5
S134
0.43
134
S1354
1354
0.58
S2354
S354
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15. Experimental Result sessions without purchase WebKDD’05 15
On average purchase sessions are longer than those
sessions without purchase
- review the information, compare the price, the quality and etc.
- fill out the billing and shipping information to commit the purchase
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16. Average Session Length
Experimental Result
SUPs in non-purchase cluster
Cluster No.
No. of Sessions
Threshold ()
Average Session Length
No. of States
SUPs 1
1746
0.3
9.6 98
1. S-C1-C1-C2-C3-C4-C5-C6-C7-E
2. S-C1-C1-C2-C3-C4-C5-E
3. S-C1-C1-C2-C3-E
4. S-C1-C2-C3-C3-C4-C5-C6-C7-E
5. S-C1-C2-C3-C4-C4-C5-C6-C7-E … 2
241
0.37
6.6 38
1. S-P1-P2-P3-P3-E
2. S-P1-P2-P3-P4-P4-P5-E
3. S-P1-P2-P3-P4-P4-E
4. S-P1-P2-P3-P4-P5-P4-E
5. S-P1-P2-P3-P4-P5-P5-E 3 13
3.0 6
1. S-C1-P1-P2-E
2. S-C1-P1-E
3. S-I1-P1-P1-P2-E
4. S-I1-P1-P1-E
5. S-I1-P1-E
Interested in gathering information of products in different categories.
S-C1-C1-C2-C3-C4-C5-C5-I1-E
S-C1-C1-I1-C1-C2-C3-C4-C5-E
S-I1-C1-C2-C3-C4-C5-C6-C7-E
Interested in reviewing general pages (to gather general information).
Not serious visitors (the average session length is 3)
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17. Experimental Result WebKDD’05 17 Cluster No. No. of Sessions Average
Length
States
Threshold
()
Beginning
Web page
SUPs in BNF Notation
Non-
Purchase 1
1746
9.6 98
0.3 S
S-{C}-E
0.25
P86806
P86806-{C}-E 2
241
6.6 38
0.37
S-{P}-[C]-E
0.34
P86806-[I]-{P}-E 3 13
3.0 6
S-<C | I>-{P}-E
0.2
P86806-[{P}- [P86806]]-E
1858
14.9 55
0.47
S-[C]-[I]-{P}-E
0.51
132
39.1
100
0.457
S -[{{C}|{I}}]-{P}-E
0.434
P86806-[{C }]-{P}-E 10
31.6 47
0.52
S-{P}-[{I}]-[{P}]-{C}-E
0.43
P86806-[I]-[{P}]-{C}-E
review the information,
compare among products,
and fill out the payment and shipping information
The average length of SUPs is
longer in the purchase cluster
than in non-purchase cluster
SUPs in the purchase cluster have
higher probability than those in
non-purchase cluster.
have purchase in mind vs.
random browsing behavior
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18. Conclusion and Future Work
Summary
- By applying clustering to abstracted user sessions, it is more
likely to find groups of users with similar motivations for
visiting a specific website.
- By giving the flexibility for user to specify the beginning
and/or ending Web page(s), users can have more control in
generating patterns of their interests.
Future
- Scalability
- Cluster to identify different user groups
- Online identification of user to predefined cluster
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19. References
[1] J. Borges and M. Levene, “Data Mining of User Navigation Patterns”, In Proc. the Workshop on Web Usage Analysis and User Profiling (WEBKDD'99), 31-36, San Diego, August 15, 1999.
[2] J. Borges and M. Levene, “An average linear time algorithm for web data mining”, International Journal of Information Technology and Decision Making, 3, (2004),
[3] W. Wang and O. R. Zaïane, “Clustering Web Sessions by Sequence Alignment”, Third International Workshop on Management of Information on the Web in conjunction with 13th International Conference on Database and Expert Systems Applications DEXA'2002, pp , Aix en Provence, France, September 2-6, 2002.

20. Thank you
Questions?
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