Table of Contents
Pattern Discovery from Usage, Content, and Structure
Web Usage Mining Process
Pattern Discovery
Common Data Mining Tasks
Sequential Patterns
Finding Frequent Navigational Patterns
Finding Frequent Navigational Patterns
Filtering Patterns Based on “Interestingness”
Finding Interesting Patterns
Example: Algorithms for Filtering Frequent Itemset
Discovering Aggregate Usage Profiles
Methodologies for the Discovery of Aggregate Profiles
Profile Aggregation Based on Clustering Transactions (PACT)
PACT - An Example
Hypergraph-Based Clustering
Attaching Weights to Hyperedges
Selection of Good Partitions
Strengths of ARHP
Usage Profiles Based on Hypergraph Clusters of Pageviews
Aggregate Usage Profiles - Hypergraph
Aggregate Usage Profiles - PACT
Integrating Content and Usage
Discovery of Content Profiles
How Content Profiles Were Generated
How Content Profiles Were Generated
How Content Profiles Were Generated
User Segments Based on Content
Another Example of Web Content Mining: WebACE (Mobasher, Boley, Gini, Han - 1998)
WebACE Architecture
What are the Transactions?
Experiments with WebACE
Feature Selection Criteria Used in the Experiments
Entropy Comparison of Algorithms
Document Clusters in AutoClass
Document Clusters in ARHP
Applying ARHP to S&P Stock Data
Clustering of S&P 500 Stock Data
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Author: Bamshad Mobasher
Email: mobasher@cs.depaul.edu
Home Page: http://maya.cs.depaul.edu/~mobasher/classes/cs589
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