CSC 466: Knowledge Discovery From Data

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CSC 466 Knowledge Discovery From Data
New Computer Science Elective

• Alex Dekhtyar
• Department of Computer Science
• Cal Poly

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Outline

• Why?
• What?
• How?
• Discussion

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Why?
Information Retrieval
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Why?
Text Classification? Link Analysis?
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Why?
Recommender Systems
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Why?
Market Basket Analysis. Purchasing trends
analysis.
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Why?
Data Warehouse and so much more
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Why?
Link Analysis
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Why?
Cluster Analysis
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Buzzwords
Data warehousing
Data mining
Market basket analysis
Web mining
Information filtering
Recommender Systems
Information retrieval
Text classification
OLAP
Cluster Analysis
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Why?
As professionals, hobbyists and consumers
students constantly interact with intelligent
information management technologies
This is moving into the realm of
undergraduate-level knowledge
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_at_Calstate.edu
CSU Fullerton CPSC 483 Data Mining and Pattern
Recognition
CSU LA CS 461 Machine Learning CS
560 Advanced Topics in Artificial Intelligence
CSU Northridge 595DM Data Mining
CSU Sacramento CSC 177. Data Warehousing and
Data Mining
CSU SF CSC 869 - Data Mining
CSU San Marcos CS475 Machine Learning
CS574 Intelligent Information
Retrieval
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What?

• Undergraduate course

Informed consumers
Professionals
OLAP/Data Warehousing
Data Mining
Knowledge Discovery from Data
Collaborative Filtering
Information Retrieval
1 quarter 10 weeks
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What? (goals)

• Understand KDD technologies _at_ consumer level
• Understand basic types of
• Data mining
• Information filtering
• Information retrieval
• techniques
• Use KDD to analyze information
• Implement KDD algorithms
• Understand/appreciate societal impacts

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What? (syllabus in a nutshell)

• Intro (data collections, measurement)
  2 lectures
• Data Warehousing/OLAP
  2 lectures
• Data Mining
• Association Rule Mining
  3 lectures
• Classification
  3 lectures
• Clustering
  3 lectures
• Collaborative Filtering/Recommendations 2
  lectures
• Information Retrieval
  4 lectures

19 lectures
CSC 466, Spring 2009 quarter
( spring quarter)
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How? (Alexs ideas)

• Learn-by-doing....
• Labs work with existing software, analyze data,
  interpret
• Labs small groups, implement simple KDD
  techniques
• Project groups, find interesting data, analyze
  it
• Need to incorporate societal issues privacy
  vs. data access, etc
• Students to make informed choices
• Lectures
• Breadth over depth
• do a follow-up CSC 560 (grad. DB topics class)

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How?
TODO List

• Find data for labs and projects
• Investigate open source mining/retrieval
  software
• Figure out the textbook
• (Web Data Mining by Bing Liu
• is promising)

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How?
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