Data Mining with Weka (5.4: Summary)

By | Y2014Y2014-9M-D

게시일: 2013. 10. 6.

Data Mining with Weka: online course from the University of Waikato
Class 5 – Lesson 4: Summary

http://weka.waikato.ac.nz/

Slides (PDF):
http://goo.gl/5DW24X

https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/

Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/


  1.   Filtered classifiers
    • Filter training data but not test data – during cross‐validation
  2.  Cost‐sensitive evaluation and classification
    • Evaluate and minimize cost, not error rate
  3.  Attribute selection
    • Select a subset of attributes to use when learning
  4.  Clustering
    • Learn something even when there’s no class value
  5.  Association rules
    • Find associations between attributes, when no “class” is specified
  6.  Text classification
    • Handling textual data as words, characters, n‐grams
  7.  Weka Experimenter
    • Calculating means and standard deviations automatically

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