1. Web Mining

2. Web Mining
Web mining is the application of data mining techniques to find interesting and potentially useful knowledge from web data.

3. What is Web Data ? Web data is Web content –text,image,records,etc.
Web structure –hyperlinks,tags,etc.
Web usage –http logs,app server logs,etc.

4. Web Mining Taxonomy Web-Mining Web Usage Mining Web Structured Mining
Web Content Mining

5. Web Content Mining
Discovery of useful information from web contents / data / documents
Web data contents:
text,
image,
audio,
video,
metadata and
hyperlinks

6. Web Structured Mining
It deal with discovering and modeling the link structure of the web.
Work has been carried out to model the web based on the topology of the hyperlinks.
Helps in
Discovering similarities between sites
In discovering important sites for a particular topic.
Discovering web communities.

7. Web Usage Mining
It deals with understanding user behavior in interacting with the web or with a website.
Aim
To obtain information that may assist web sites for reorganization or adaptation to better suit the user.

8. To understand user’s behaviour
Clicking pattern
Browsing time
Transaction

9. Application Target potential customers for electronic commerce
Enhance the quality and delivery of Internet information services to the end user
Improve Web server system performance
Identify potential prime advertisement locations
Facilitates personalization/adaptive sites
Improve site design
Fraud/intrusion detection
Predict user’s actions (allows pre fetching)

10. Web Mining Taxonomy Application Level Logs Web-Mining Web Usage Mining
Web Structured Mining
Web Content Mining
Text
Image
Audio
Video
Structured
Web-Server Logs
Application Server Logs
Document Structured
Hyperlinks
Intra-Document Hyperlinks
Inter-Document Hyperlinks

11. Web usage mining and E-Commerce
E-commerce is the killer-application of web mining
Keep former customers and attract new customers
Provide better service and be more interactive
Web usage mining is the best way to analyse the customer’s behaviour.
Discover customers needs or interests
Analyse customers behaviour

12. Pattern discovery and analysis
The KDD Process for E-commerce
Action
Data collection and
Pre-processing
Pattern discovery and analysis
Reconditions
Again Action
Mining

13. Pattern Discovery and Analysis
Using the mining algorithms to discover the pattern
Pattern Analysis
To filter out uninteresting/meaningless rules or patterns from the set found in the pattern discovery phase
Information filter
OLAP (On-line analytical processing)
Visualization
Knowledge query mechanism (SQL)

14. Technologies For Web Usage Mining
Web usage mining technologies
Statistical analysis
Most common method, such as frequency, mean (average), median, etc.
Classification
Mapping a data item into one of several predefined classes
Clustering
To group together a set of items having similar characteristics

15. Technologies For Web Usage Mining
Association rule
Can be used to relate page or product that are most often referenced or purchased together
Sequential patterns
A set of items is followed by another item in time-order

16. E-commerce Business Objectives
Personalization
Web site personalization (content or layout)
Personalized advertisement
Personalized product recommendation
Marketing strategy
Marketing rule
Changing the marketing strategy
Web site design
Web site evaluation
Reorganize
Improve the hypertext structure
Optimization

17. Web usage mining for e-commerce
Many applications in different areas of E-commerce have already been proposed
However, most research just focuses on the first two steps of the KDD process
Data mining is meaningless if we do not take action in E-commerce

18. In the area of web usage mining for
Possible work
In the area of web usage mining for
E-commerce. Also in the area of web search
employing Web Crawlers or algorithms like
HITS (Hypertext Induced topic search), Web
Warehousing etc.