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Data Mining
By
Dave Maung
What is Data Mining?

The process of automatically searching large
volumes of data for patterns.
 Also known as KDD Knowledge-Discovery.
Different types of Data Mining
 Relational
data mining
 Text mining
 Web mining
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

Classification
Predicting an item class
 Finding rules that partition the given data
into disjoints groups
 Popular classification Methods is
decision tree

Decision Tree
A graph of decisions and their possible
consequences
 Decision trees are constructed to help
making decisions.
 A decision tree used tree structure.

Example of Decision Tree
Text Mining

Is the process of
extracting interesting
 non-trivial information
 knowledge from unstructured text

Text Mining (continued)

Also known as
 intelligent text analysis
 text data mining
 unstructured data management
 or knowledge-discovery in text
Web Mining
Is the extraction of interesting potentially
useful patterns
 Implicit information from artifacts
 Activity related to the Worldwide Web

Web Mining (continued)

Three knowledge discovery domains that
pertain to web mining
Web Content Mining,
 Web Structure Mining,
 Web Usage Mining

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.

Web Structure Mining

Is Worldwide Web can reveal more
information than just the information
contained in documents
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.

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

Web Usage Mining
 Two
main tendencies in Web Usage
Mining driven:
 General Access Pattern Tracking
 Customized Usage Tracking
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

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.

Web Mining Architecture
Reference

http://wikipedia.com