csc 466 knowledge discovery from data n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
CSC 466: Knowledge Discovery From Data PowerPoint Presentation
Download Presentation
CSC 466: Knowledge Discovery From Data

Loading in 2 Seconds...

play fullscreen
1 / 18

CSC 466: Knowledge Discovery From Data - PowerPoint PPT Presentation


  • 111 Views
  • Uploaded on

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?.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'CSC 466: Knowledge Discovery From Data' - heidi


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
Outline
  • Why?
  • What?
  • How?
  • Discussion
slide3
Why?

Information Retrieval

slide4
Why?

Text Classification? Link Analysis?

slide5
Why?

Recommender Systems

slide6
Why?

Market Basket Analysis. Purchasing trends analysis.

slide7
Why?

Data Warehouse… and so much more…

slide8
Why?

Link Analysis

slide9
Why?

Cluster Analysis

buzzwords
Buzzwords

Data warehousing

Data mining

Market basket analysis

Web mining

Information filtering

Recommender Systems

Information retrieval

Text classification

OLAP

Cluster Analysis

slide11
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
@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

slide13
What?
  • Undergraduate course

Informed consumers

Professionals

OLAP/Data Warehousing

Data Mining

Knowledge

Discovery

from Data

Collaborative Filtering

Information Retrieval

1 quarter = 10 weeks

what goals
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
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)

how alex s ideas
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)
slide17
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)

slide18
How?

This slide intentionally left blank