sprint a scalable parallel classifier for data mining n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
SPRINT: A Scalable Parallel Classifier for Data Mining PowerPoint Presentation
Download Presentation
SPRINT: A Scalable Parallel Classifier for Data Mining

Loading in 2 Seconds...

play fullscreen
1 / 11

SPRINT: A Scalable Parallel Classifier for Data Mining - PowerPoint PPT Presentation


  • 99 Views
  • Uploaded on

SPRINT: A Scalable Parallel Classifier for Data Mining. Presenter : Yu-hui Huang Authors : John Shafer , Rakesh Agrawal Manish Mehta. 國立雲林科技大學 National Yunlin University of Science and Technology. VLDB 1996. Outline. Motivation Objective Methodology Experiment Conclusion.

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 'SPRINT: A Scalable Parallel Classifier for Data Mining' - marv


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
sprint a scalable parallel classifier for data mining

SPRINT: A Scalable Parallel Classifier for Data Mining

Presenter : Yu-hui Huang

Authors : John Shafer , Rakesh Agrawal Manish Mehta

國立雲林科技大學

National Yunlin University of Science and Technology

VLDB 1996

outline
Outline
  • Motivation
  • Objective
  • Methodology
  • Experiment
  • Conclusion
motivation
Motivation
  • Run time is expensive
  • must remain memory resident at all times.
  • Require large memory

Data set

objective
Objective
  • Construct a algorithm can to handle large datasets
  • Allowing many processors to work together
methodology sprint1
Methodology-SPRINT

27.5 <--------------------------------------------------------------------------

methodology sliq
Methodology-SLIQ
  • SLIQ:
  • Parallelizing SLIQ:
    • SLIQ/R: the class list is replicated in the memory of every processor
    • SLIQ/D: Each.processor therefore contains only l/Nth of the class list.
conclusion
Conclusion
  • The SPRINT is no memory restrictions
  • Run time is very fast , compare with previous algorithm.

10

comments
Comments
  • Advantage
  • Drawback
    • ….
  • Application
    • medical diagnosis , fraud detection, retail target marketing…