ClusteringApplications in Web Mining and

Web Personalization

Bamshad MobasherDePaul University

Bamshad MobasherDePaul University

2

Clustering Application: Web Usage Mining

i Discovering Aggregate Usage Profiles4 Goal: to effectively capture “user segments” based on their common usage patterns

from potentially anonymous click-stream data4 Method: Cluster user transactions to obtain user segments automatically, then

represent each cluster by its centroidh Aggregate profiles are obtained from each centroid after sorting by weight and filtering

out low-weight items in each centroid

4 Note that profiles are represented as weighted collections of items (pages, products, etc.)h weights represent the significance of the item within each cluster

4 profiles are overlapping, so they capture common interests among different groups/types of users (e.g., customer segments)

3

Profile Aggregation Based on Clustering Transactions (PACT)

i Discovery of Profiles Based on Transaction Clusters4 cluster user transactions - features are significant items present in the

transaction 4 derive usage profiles (set of item-weight pairs) based on characteristics of each

transaction cluster as captured in the cluster centroid

i Deriving Usage Profiles from Transaction Clusters4 each cluster contains a set of user transactions (vectors)4 for each cluster compute centroid as cluster representative

4 a set of item-weight pairs: for transaction cluster C, select each pageview pi such that (in the cluster centroid) is greater than a pre-specified thresholdc

iu

4

PACT - An ExampleA.html B.html C.html D.html E.html F.html

user0 1 1 0 0 0 1user1 0 0 1 1 0 0user2 1 0 0 1 1 0user3 1 1 0 0 0 1user4 0 0 1 1 0 0user5 1 0 0 1 1 0user6 1 1 0 0 0 1user7 0 0 1 1 0 0user8 1 0 1 1 1 0user9 0 1 1 0 0 1

A.html B.html C.html D.html E.html F.htmlCluster 0 user 1 0 0 1 1 0 0

user 4 0 0 1 1 0 0user 7 0 0 1 1 0 0

Cluster 1 user 0 1 1 0 0 0 1user 3 1 1 0 0 0 1user 6 1 1 0 0 0 1user 9 0 1 1 0 0 1

Cluster 2 user 2 1 0 0 1 1 0user 5 1 0 0 1 1 0user 8 1 0 1 1 1 0

PROFILE 0 (Cluster Size = 3)--------------------------------------1.00 C.html1.00 D.html

PROFILE 1 (Cluster Size = 4)--------------------------------------1.00 B.html1.00 F.html0.75 A.html0.25 C.html

PROFILE 2 (Cluster Size = 3)--------------------------------------1.00 A.html1.00 D.html1.00 E.html0.33 C.html

Original Session/user

data

Result ofClustering

Given an active session A B, the best matching profile is Profile 1. This may result in a recommendation for page F.html, since it appears with high weight in that profile.

Given an active session A B, the best matching profile is Profile 1. This may result in a recommendation for page F.html, since it appears with high weight in that profile.

5

Web Usage Mining: clustering example

i Transaction Clusters: 4 Clustering similar user transactions and using centroid of each cluster as an

aggregate usage profile (representative for a user segment)

Support URL Pageview Description

1.00 /courses/syllabus.asp?course=450-96-303&q=3&y=2002&id=290

SE 450 Object-Oriented Development class syllabus

0.97 /people/facultyinfo.asp?id=290 Web page of a lecturer who thought the above course

0.88 /programs/ Current Degree Descriptions 2002

0.85 /programs/courses.asp?depcode=96&deptmne=se&courseid=450

SE 450 course description in SE program

0.82 /programs/2002/gradds2002.asp M.S. in Distributed Systems program description

Sample cluster centroid from dept. Web site (cluster size =330)

6

Clustering Application: Discovery of Content Profiles

i Content Profiles4 Goal: automatically group together documents which partially deal with

similar concepts4 Method:

h identify concepts by clustering features (keywords) based on their common occurrences among documents (can also be done using association discovery or correlation analysis)

h cluster centroids represent docs in which features in the cluster appear frequently

4 Content profiles are derived from centroids after filtering out low-weight docs in each centroid

4 Note that each content profile is represented as a collections of item-weight pairs (similar to usage profiles)h however, the weight of an item in a profile represents the degree to which features in

the corresponding cluster appear in that item.

7

Content Profiles – An Example

PROFILE 0 (Cluster Size = 3)--------------------------------------------------------------------------------------------------------------1.00 C.html (web, data, mining)1.00 D.html (web, data, mining)0.67 B.html (data, mining)

PROFILE 1 (Cluster Size = 4)-------------------------------------------------------------------------------------------------------------1.00 B.html (business, intelligence, marketing, ecommerce)1.00 F.html (business, intelligence, marketing, ecommerce)0.75 A.html (business, intelligence, marketing)0.50 C.html (marketing, ecommerce)0.50 E.html (intelligence, marketing)

PROFILE 2 (Cluster Size = 3)-------------------------------------------------------------------------------------------------------------1.00 A.html (search, information, retrieval)1.00 E.html (search, information, retrieval)0.67 C.html (information, retrieval)0.67 D.html (information, retireval)

Filtering threshold = 0.5

8

User Segments Based on Contenti Essentially combines usage and content profiling techniques

discussed earlier

i Basic Idea:4 for each user/session, extract important features of the selected

documents/items4 based on the global dictionary create a user-feature matrix4 each row is a feature vector representing significant terms associated with

documents/items selected by the user in a given session4 weight can be determined as before (e.g., using tf.idf measure)4 next, cluster users/sessions using features as dimensions

i Profile generation:4 from the user clusters we can now generate overlapping collections of features

based on cluster centroids4 the weights associated with features in each profile represents the significance

of that feature for the corresponding group of users.

9

A.html B.html C.html D.html E.html

user1 1 0 1 0 1

user2 1 1 0 0 1

user3 0 1 1 1 0

user4 1 0 1 1 1

user5 1 1 0 0 1

user6 1 0 1 1 1

A.html B.html C.html D.html E.html

web 0 0 1 1 1

data 0 1 1 1 0

mining 0 1 1 1 0

business 1 1 0 0 0

intelligence 1 1 0 0 1

marketing 1 1 0 0 1

ecommerce 0 1 1 0 0

search 1 0 1 0 0

information 1 0 1 1 1

retrieval 1 0 1 1 1

User transaction matrix UT

Feature-DocumentMatrix FP

10

Content Enhanced Transactions

web data mining business intelligence marketing ecommerce search information retrieval

user1 2 1 1 1 2 2 1 2 3 3

user2 1 1 1 2 3 3 1 1 2 2

user3 2 3 3 1 1 1 2 1 2 2

user4 3 2 2 1 2 2 1 2 4 4

user5 1 1 1 2 3 3 1 1 2 2

user6 3 2 2 1 2 2 1 2 4 4

User-FeatureMatrix UF Note that: UF = UT x FPT

Example: users 4 and 6 are more interested in concepts related to Web information retrieval, while user 3 is more interested in data mining.

11

Clustering and Collaborative Filtering :: Example - clustering based on ratings

 TRUE

BELIEVERTHE DA VINCI

CODETHE WORLD IS

FLATMY LIFE SO

FARTHE

TAKINGTHE KITE RUNNER

RUNNY BABBIT

HARRY POTTER

U1 1 5 3   3 5U2 5 4 3 2 1  U3 3 1 2 2     5U4   3     4 1   3U5 2 4 3     2 2  U6 5     3 1   3 1U7 1 4 5 5 2     4U8 2 1     4 5 1  U9   3 2 2     5

U10 3 5 1       4 4U11 2 1   2   3U12 4 4   2   1 1 4U13 2   4   4 5U14 5 3 3 2   1 1U15   2     3 3   2U16   3 2 1 1   4 4U17 1 5 1 2   4   4U18 5 4   3 3 4 5U19 4   2   5 1 5U20 2 5 1 1 5 3   4

Consider the following book ratings data (Scale: 1-5)

12

Clustering and Collaborative Filtering :: Example - clustering based on ratings

Cluster centroids after k-means clustering with k=4• In this case, each centroid represented the

average rating (in that cluster of users) for each item• The first column shows the centroid of the whole data

set, i.e., the overall item average ratings across all users

Full Data Cluster 0 Cluster 1 Cluster 2 Cluster 3

Size=20 Size=4 Size=7 Size=4 Size=5TRUE BELIEVER 2.83 4.21 1.81 2.83 3.17

THE DA VINCI CODE 3.86 4.21 3.84 2.96 4.31

THE WORLD IS FLAT 2.33 2.50 2.71 2.17 1.80

MY LIFE SO FAR 2.25 2.56 2.64 1.63 1.95

THE TAKING 2.77 2.19 2.73 2.69 3.35

THE KITE RUNNER 2.82 2.16 3.49 2.20 2.89

RUNNY BABBIT 2.42 1.50 2.04 2.81 3.37

HARRY POTTER 3.76 2.44 4.36 3.00 4.60

13

Clustering and Collaborative Filtering :: Example - clustering based on ratings

This approach provides a model-based (and more scalable) versionof user-based collaborative filtering, compared to k-nearest-neighbor

  Full Data Cluster 0 Cluster 1 Cluster 2 Cluster 3   New User NU1  Size=20 Size=4 Size=7 Size=4 Size=5  TRUE BELIEVER 2.83 4.21 1.81 2.83 3.17   3.00THE DA VINCI CODE 3.86 4.21 3.84 2.96 4.31    THE WORLD IS FLAT 2.33 2.50 2.71 2.17 1.80   3.00MY LIFE SO FAR 2.25 2.56 2.64 1.63 1.95    THE TAKING 2.77 2.19 2.73 2.69 3.35   4.00THE KITE RUNNER 2.82 2.16 3.49 2.20 2.89   3.00RUNNY BABBIT 2.42 1.50 2.04 2.81 3.37    HARRY POTTER 3.76 2.44 4.36 3.00 4.60   4.00               Correlation w/ NU1 0.63 -0.41 0.50 0.65 0.74    

NU1 has highest similarity to cluster 3 centroid. The whole cluster couldbe used as the “neighborhood” for NU1.

14

Clustering and Collaborative Filtering :: clustering based on ratings: movielens

15

Clustering on the Social Web :: tag clustering example

16

Hierarchical Clustering:: example – clustered search results

Can drill down within clusters to view sub-topics or to view the relevant subset of results

ClusteringApplications in Web Mining and

Web Personalization

Bamshad MobasherDePaul University

Bamshad MobasherDePaul University