1. CSC 466: Knowledge Discovery From Data Alex Dekhtyar Department of Computer Science Cal Poly New Computer Science Elective

2. Outline  Why?  What?  How?  Discussion

3. Why? Information Retrieval

4. Why? Text Classification? Link Analysis?

5. Why? Recommender Systems

6. Why? Market Basket Analysis. Purchasing trends analysis.

7. Why? Data Warehouse… and so much more…

8. Why? Link Analysis

9. Why? Cluster Analysis

10. Buzzwords Data warehousing Data mining Information filtering Recommender Systems Information retrieval Text classification Web mining OLAP Cluster Analysis Market basket analysis

11. Why? As professionals, hobbyists and consumers students constantly interact with intelligent information management technologies This is moving into the realm of undergraduate-level knowledge

12. @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

13. What?  Undergraduate course Informed consumers Professionals OLAP/Data Warehousing Data Mining Collaborative Filtering Information Retrieval 1 quarter = 10 weeks Knowledge Discovery from Data

14. What? (goals)  Understand KDD technologies @ 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

15. 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 (= spring quarter) CSC 466, Spring 2009 quarter

16. How? (Alex’s 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)

17. 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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