CSC 466: Knowledge Discovery From Data Alex Dekhtyar Department of Computer Science Cal Poly New...
-
date post
21-Dec-2015 -
Category
Documents
-
view
214 -
download
0
Transcript of CSC 466: Knowledge Discovery From Data Alex Dekhtyar Department of Computer Science Cal Poly New...
CSC 466: Knowledge Discovery From Data
Alex DekhtyarDepartment of Computer Science Cal Poly
New Computer Science Elective
Outline
Why?
What?
How?
Discussion
Why?
Information Retrieval
Why?
Text Classification? Link Analysis?
Why?
Recommender Systems
Why?
Market Basket Analysis. Purchasing trends analysis.
Why?
Data Warehouse… and so much more…
Why?
Link Analysis
Why?
Cluster Analysis
Buzzwords
Data warehousing Data mining
Information filtering
Recommender SystemsInformation retrieval
Text classification
Web mining
OLAP Cluster Analysis
Market basket analysis
Why?
As professionals, hobbyists and consumers students constantly interact with intelligent information management technologies
This is moving into the realm of undergraduate-level knowledge
@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
What?
Undergraduate course
Informed consumers Professionals
OLAP/Data Warehousing
Data Mining
Collaborative Filtering
Information Retrieval
1 quarter = 10 weeks
Knowledge Discoveryfrom Data
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
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
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)
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)
How?
This slide intentionally left blank