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Data Mining - Classification and Prediction - Issues, Study notes of Data Mining

In this document topics covered which are Classification and Prediction, Evaluating Classification Methods, Accuracy, Speed, Robustness, Scalability.

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2010/2011
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November 27, 2014 Data Mining: Concepts and
Techniques 1
Chapter 6. Classification and Prediction
What is classification? What is
prediction?
Issues regarding classification
and prediction
Classification by decision tree
induction
Bayesian classification
Rule-based classification
Classification by back
propagation
Support Vector Machines
(SVM)
Associative classification
Lazy learners (or learning from
your neighbors)
Other classification methods
Prediction
Accuracy and error measures
Ensemble methods
Model selection
Summary
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November 27, 2014 Data Mining: Concepts and 1

Chapter 6. Classification and Prediction

  • (^) What is classification? What is prediction?
  • (^) Issues regarding classification and prediction
  • (^) Classification by decision tree induction
  • (^) Bayesian classification
  • (^) Rule-based classification
  • (^) Classification by back propagation - (^) Support Vector Machines (SVM) - (^) Associative classification - (^) Lazy learners (or learning from your neighbors) - (^) Other classification methods - (^) Prediction - (^) Accuracy and error measures - (^) Ensemble methods - (^) Model selection - Summary

November 27, 2014 Data Mining: Concepts and 2

Issues: Data Preparation

  • (^) Data cleaning
    • (^) Preprocess data in order to reduce noise and handle missing values by applying smoothing techniques
  • (^) Relevance analysis (feature selection)
    • (^) Many of the attributes in the data may be redundant. Correlation analysis can be used to identify whether any two given attributes are statistically related.
    • (^) Remove the irrelevant or redundant attributes by Attribute subset selection
  • (^) Data transformation and reduction
    • (^) Generalize and/or normalize data