LIBR 246
Text/Data/Web Mining for LIS
Course Calendar

Topic

Readings & Dues

Orientation

• Course overview & logistics
• Teaching approach
• Choosing a track of study focus & group formation
• Textbook for chosen track of study focus

Elluminate session
8/25 9:00-11:45am

Introduction

• What text/data/web (TDW) mining is about
• TDW mining vs. Information Retrieval
• Toward knowledge/intelligence discovery
• TDW mining in relation to LIS

Zanasi, Ch. 1;
Han & Kamber, Ch. 1;
Kroeze, , Matthee, & Bothma (2003).

Concepts, Theories & Approaches (by tracks of study focus)

• NLP-based text mining
• Statistical text mining
• Data mining
• Web use & transaction log mining

Zanasi, Ch. 2
Han & Kamber, Ch. 2;
Markov & Larose, Ch. 1, 6

Summary #1 DUE
9/16 11:30pm

Survey of text/data mining software

• TDW mining software tools
• SPSS as a data mining instrument
• Visualization function and setting
• Selection of software tools
• What we are going to use: CALAIS, Excel, Rapid Miner (or SPSS)

Zanasi, Ch. 7 & 21

Summary#2 DUE
9/23 11:30pm

Text/data/transaction mining for LIS

• Needs identification 
• From document service to information service 
• Active reference services 
• A new information profession
• Mining of library patron/use data 

Collier (2003);
Dhiman (2003);
Guenther (2000);
Zanasi, Ch. 19

Summary #3 DUE
9/30 11:30pm

Ethical and social issues of text/data/web mining

• Privacy concern
• Legal and security issues of collecting and storing large quantity of data
• Larson’s laws of data dynamics
• Fair information principles (Purpose, Fairness, Transparency)
• Ethical issues of mining library patron/transaction data

 Larson (1992);
Van Wel (2004)

Summary #4 DUE
10/7 11:30pm

Elluminate Meeting
10/13 9:00-11:45am

Group Report DUE
10/14 11:30pm

Preparing for Individual Exercise/Project

• Install/configure CLAIS, RapidMiner, and/or SPSS
• Learning about GUI interface/features of mining software
• Downloading/constructing data set 
• Understanding variables

Statistical Data Analysis

• Data types and the concept of "variable"
• Plotting and visual data exploration
• Descriptive and inferential statistical analyses
• Statistical models & hypothesis testing
• Correlation and regression analysis

(Lecture)

Advanced Data Mining Techniques

• Overview of data mining process
• Decision tree and association rules
• Clustering and classificaiton
• Time series analysis
• Neural network traning & testing

Individual Mining Project

• Overview of data set 
• Data mining approach and model 
• Documentation of process 
• Analysis and findings

.

Competitive intelligence

• Corporative needs of intelligence 
• Gleaning of competitive intelligence from public domain sources

Zanasi, Ch. 8-11;
Fleisher & Bensoussan (2003).
Herring (1998);

Individual Mining Project Report

Elluminate session
12/8 9:00-11:45am

DUE: 12/10 11:30pm