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CSC 466: Knowledge Discovery From Data

CSC 466: Knowledge Discovery From Data. New Computer Science Elective. Alex Dekhtyar Department of Computer Science Cal Poly. Outline. Why? What? How? Discussion. Why?. Information Retrieval. Why?. Text Classification? Link Analysis?. Why?. Recommender Systems. Why?.

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CSC 466: Knowledge Discovery From Data

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

  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 Market basket analysis Web mining Information filtering Recommender Systems Information retrieval Text classification OLAP Cluster 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 Knowledge Discovery from Data Collaborative Filtering Information Retrieval 1 quarter = 10 weeks

  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 CSC 466, Spring 2009 quarter (= spring 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)

  18. How? This slide intentionally left blank

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