1. Data Mining By
Dave Maung

2. What is Data Mining?
The process of automatically searching large volumes of data for patterns.
Also known as KDD Knowledge-Discovery.

3. Different types of Data Mining
Relational data mining
Text mining
Web mining

4. Relational Data Mining
Data mining technique for relational databases
Relational data mining algorithms look for patterns among multiple tables
Used classification rules and Association rules

5. Classification Predicting an item class
Finding rules that partition the given data into disjoints groups
Popular classification Methods is decision tree

6. Decision Tree A graph of decisions and their possible consequences
Decision trees are constructed to help making decisions.
A decision tree used tree structure.

7. Example of Decision Tree

8. Text Mining Is the process of extracting interesting
non-trivial information
knowledge from unstructured text

9. Text Mining (continued)
Also known as
intelligent text analysis
text data mining
unstructured data management
or knowledge-discovery in text

10. Web Mining
Is the extraction of interesting potentially useful patterns
Implicit information from artifacts
Activity related to the Worldwide Web

11. Web Mining (continued)
Three knowledge discovery domains that pertain to web mining
Web Content Mining,
Web Structure Mining,
Web Usage Mining

12. Web Content Mining
Is an automatic process that goes beyond keyword extraction.
There are two groups of web content mining strategies:
mine the content of documents
improve on the content search of other tools like search engines.

13. Web Structure Mining
Is Worldwide Web can reveal more information than just the information contained in documents

14. Web Structure Mining (example)
Links pointing to a document indicate the popularity of the document.
Links coming out of a document indicate the richness or perhaps the variety of topics covered in the document.

15. Web Usage Mining
Web servers record and accumulate data about user interactions whenever requests for resources are received.
Analyzing the web access logs of different web sites

16. Web Usage Mining Two main tendencies in Web Usage Mining driven:
General Access Pattern Tracking
Customized Usage Tracking

17. General access pattern
Analyzes the web logs to understand access patterns and trends
Give better structure and grouping of resource providers
Can be used to restructure sites in a more efficient grouping, and target specific users for specific selling ads

18. Customized usage tracking
Analyzes individual trends
To customize web sites to users
Success of Application depends on what and how much valid and reliable knowledge one can discover from the large raw log data.

19. Web Mining Architecture

20. Reference