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The Australian National University
ANU College of Engineering and Computer Science
Department of Computer Science
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Course content

The course will cover the following topics:

  1. Course introduction and data mining overview (1 hour)
  2. Data mining process (1 hour)
  3. Data issues in data mining (including data warehouses) and data pre-processing (2 hours)
  4. Data integration and linkage (2 hours)
  5. Mining frequent patterns and associations (2 hours)
  6. Cluster analysis (2 hours)
  7. Classification and prediction: Decision trees, Bayes classification, neural networks, support vector machines, predictive modelling, accuracy and evaluation measures, lazy and eager learner (4 hours)
  8. Outlier detection (1 hour)
  9. Privacy-preserving data mining (1 hour)
  10. Text data mining (1 hour)
  11. Web data mining (1 hour)
  12. Mining time series and data streams (1 hour)
  13. Data mining trends, social impacts and course review (1 hour)

Depending upon the final schedule, one or two guest lectures will be added at the end of the semester.

Course schedule (last update 12 February 2013)

Semester week
(year week)
Date
Lecture
Tutorials / Labs
Assignments
1 (8) 19 Feb Course introduction and
data mining overview;
The data mining process;
No lab or tutorial.
2 (9) 26 Feb

Data issues in data mining;
Data pre-processing

No lab or tutorial.
3 (10) 5 Mar Data integration and
data linkage
Laboratory 1
(Introduction to Rattle)
Assignment 1 released
4 (11) 12 Mar Mining frequent patterns and associations Tutorial 1
(Rahm and Do, 2000: Data cleaning)
5 (12) 19 Mar Cluster analysis
Laboratory 2
(Association rules in Rattle)
6 (13) 26 Mar Classification and prediction (1) Tutorial 2
(Agrawal and Srikant, 1994: Fast Algorithms for Mining Association Rules)
Assignment 1 due
(Wednesday 27 March, 5 pm)
Assignment 2 released
Mid Semester Break (Friday 29 March to Sunday 14 April)
7 (16) 16 Apr No lectures - Peter at PAKDD conference
8 (17) 23 Apr Classification and prediction (2) Laboratory 3
(Decision trees in Rattle)

9 (18) 30 Apr
Outlier detection;
Privacy-preserving data mining
Tutorial 3
(Hand, 2006: Classifier technology)

10 (19) 7 May Text data mining;
Web data mining
Laboratory 4
(SVM and other classifiers in Rattle)
11 (20) 14 May Mining data streams and time series;
Data mining trends, social impacts and course review

Tutorial 4
(Verykios et al., 2004: Privacy-preserving data mining)
12 (21) 21 May Student research paper presentations;
Guest lecture (Graham Williams, End-to-end data mining, data mining at the ATO)
Student research paper presentations Assignment 2 due
(Wednesday 22 May, 5 pm)
13 (22) 28 May

Student research paper presentations;
Data mining trends, social impacts and course review


Student research paper presentations Paper presentation report due
(Wednesday 29 May, 5 pm)

Last modified: 18/02/2013, 15:31