1. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data
Srivastava J., Cooley R., Deshpande M, Tan P.N.
Appeared in SIGKDD Explorations, Vol. 1, Issue 2, 2000

2. Web Mining What is? What kind of?
Data Mining efforts associated with the Web
What kind of?
Content Mining
Structure Mining
Usage Mining

3. Web Data Content Structure Usage User profile Ex) texts and graphics
Ex) HTML tags
Usage
Ex) IP address, page reference, date/time
User profile
Ex) registration data, customer profile

4. Web Usage Mining
The application of data mining techniques to discover usage patterns from Web Data.
Three phrases
Preprocessing
Pattern discovery
Pattern analysis

5. Data Sources Where the usage data can be collected from?
Server Level Collections
The web server log records the browsing behavior of site visitors, but cached page views are not recorded.
The packet sniffing extracts usage data directly from TCP/IP packets.

6. <Sample Web Server Log>
Data Sources (contd.)
<Sample Web Server Log>
# IP Address Userid Time Method/ URL/ Protocol Status Size Referrer Agent
[25/Apr/1998:03:04: ] "GET A.html HTTP/1.0" Mozilla/3.04 (Win95, I)
[25/Apr/1998:03:05: ] "GET B.html HTTP/1.0" A.html Mozilla/3.04 (Win95, I)
[25/Apr/1998:03:05: ] "GET L.html HTTP/1.0" Mozilla/3.04 (Win95, I)
[25/Apr/1998:03:06: ] "GET F.html HTTP/1.0" B.html Mozilla/3.04 (Win95, I)
[25/Apr/1998:03:06: ] "GET A.html HTTP/1.0" Mozilla/3.01 (X11, I, IRIX6.2, IP22)
[25/Apr/1998:03:07: ] "GET B.html HTTP/1.0" A.html Mozilla/3.01 (X11, I, IRIX6.2, IP22)
[25/Apr/1998:03:07: ] "GET R.html HTTP/1.0" L.html Mozilla/3.04 (Win95, I)
[25/Apr/1998:03:09: ] "GET C.html HTTP/1.0" A.html Mozilla/3.01 (X11, I, IRIX6.2, IP22)
[25/Apr/1998:03:10: ] "GET O.html HTTP/1.0" F.html Mozilla/3.04 (Win95, I)
[25/Apr/1998:03:10: ] "GET J.html HTTP/1.0" C.html Mozilla/3.01 (X11, I, IRIX6.2, IP22)
[25/Apr/1998:03:12: ] "GET G.html HTTP/1.0" B.html Mozilla/3.04 (Win95, I)
[25/Apr/1998:05:05: ] "GET A.html HTTP/1.0" Mozilla/3.04 (Win95, I)
[25/Apr/1998:05:06: ] "GET D.html HTTP/1.0" A.html Mozilla/3.04 (Win95, I)

7. Data Sources (contd.) Client Level Collections By using remote agents
ex) java applet (overhead), java script (not able to capture all user clicks)
By modifying the source code of existing browser
ex) Mosaic (hard to convince users to use browser)

8. Data Sources (contd.) Proxy Level Collections
Intermediate level of caching between web server and client browser.
Characterize the browsing behavior of a group of users sharing a common proxy server.

9. Data Abstractions
User : a single individual that is accessing file from one or more Web servers through a browser
Page Views : every file displayed on user’s browser at one time
Click Stream : a sequential series of page view requests
User Session : the click stream of page views for a single user across the entire Web
Server Session : the set of page views in a user session for a particular Web site
Episode : any semantically meaningful subset of a user or server session

10. Web Usage Mining Process

11. Preprocessing Usage Processing
The most difficult task due to the incompleteness of the available data (IP address, agent, server side click stream)
Single IP address/Multiple Server Sessions
Multiple IP address/Single Server Session
Multiple IP address/Single User
Multiple Agent/Single User

12. Preprocessing(contd.)
Content Preprocessing
Converting the text, image, scripts into useful forms (ex. vectors of words)
Classification/clustering algorithm can be used to filter discovered patterns based on topic or intended use
Structure Preprocessing
Hyperlinks between page views

13. Pattern Discovery Statistical Analysis Association Rules Clustering
Page views, viewing time, length of navigational path
Association Rules
Apriori algorithm: correlation between users
Clustering
Usage clustering : inferring user demographics
Page clustering: pages having related content

14. Pattern Discovery (contd.)
Classification
30% of users who placed an online order in /Product/Music are in the age group and live on the West Coast.
Sequential Patterns
Time-ordered set of sessions: predicting future visit patters for where to put advertisement

15. Pattern Analysis Motivation
Filter out uninteresting rules / patterns from the set found in the pattern discovery phrase.

16. Application Areas

17. Examples Personalization Business
Business