This course covers various aspects of data mining including data preprocessing, classification, ensemble methods, association rules, sequence mining, and cluster analysis. The class project involves hands-on practice of mining useful knowledge from a large data set.
[04/12/2012] All class material is now available online. Good luck for the project and final exam preparation!
(Future lectures and events are tentative.)
| Date | Topic | Remarks and Homework |
| Jan 9 | Introduction; Data Preprocessing | Read chapters 1 (introduction), 2 (getting to know your data), and 3 (preprocessing) in the textbook. To get started with Weka and experience what we discussed in the lecture, do the optional homework that is available on Blackboard. |
| Jan 16 | No class: MLK Day | |
| Jan 23 | Classification/Prediction: decision trees and overfitting | Read relevant sections in chapter 8 (classification: basic concepts). Make sure you know what overfitting means and that you understand the overfitting example (big versus small tree) we discussed in class. |
| Jan 30 | Decision trees; statistical learning theory | Read relevant sections in chapter 8 (classification: basic concepts). For more information, also look at references [1] for trees and [5] for statistical decision theory (see below). |
| Feb 6 | Statistical learning theory; nearest neighbor; Bayes' theorem | Read relevant sections in chapter 8 (classification: basic concepts) and 9 (classification: advanced methods). For more information, also look at reference [5] for statistical decision theory (see below). |
| Feb 7 | HW 1 due (11pm) | |
| Feb 13 | Naive Bayes; joint distribution; Bayesian networks | Read relevant sections in chapter 8 (classification: basic concepts) and 9 (classification: advanced methods). For more information about Bayesian classification, also look at the other references, e.g., [2] and [6]. Go over the Naive Bayes example and the examples for computing probabilities of interest from the joint probability table. Make sure you can compute such probabilities for a given example. |
| Feb 20 | No class: Presidents' Day | |
| Feb 24 |
Extra class: makeup
day for Monday holidays Bayesian networks; artificial neural networks |
Read relevant sections in chapter 9 (classification: advanced methods). For more information about Bayesian classification, also look at the other references, e.g., [2] and [6]. Go carefully over the Bayesian network computation examples and also the backpropagation example in the textbook. |
| Feb 27 | SVMs; regression | Read relevant sections in chapter 9 (classification: advanced methods). If you are interested in more information about SVMs, let me know and I can point you to an interesting survey article. |
| Mar 1 | HW 2 due (11pm) | |
| Mar 5 | No class: Spring Break | |
| Mar 12 | Midterm Exam | Same time and location as lectures. |
| Mar 19 | Accuracy and error measures; ensemble methods | Study the various model quality measures. Read section 8.5 (model evaluation and selection) and the beginning of section 8.6 (techniques to improve classification accuracy). |
| Mar 26 | Ensemble methods; frequent pattern mining | Read section 8.6 (techniques to improve classification accuracy) and relevant sections in chapter 6 (mining frequent patterns, associations, and correlations). Run the Apriori algorithm manually on a small example. Observe when and how it is pruning the search space. |
| Apr 2 | Frequent pattern mining | Read the relevant sections in chapters 6 (mining frequent patterns, associations, and correlations) and 7 (advanced frequent pattern mining). For FP-growth and sequence mining, focus on the main ideas, not the algorithmic details. |
| Apr 7 | Project report 1 due (11pm) | |
| Apr 9 | Clustering | Read the relevant sections in chapters 10 (cluster analysis: basic concepts and methods) and 11 (advanced cluster analysis). |
| Apr 14 | Project report 2 due (11pm) | |
| Apr 16 | No class: Patriots' Day | |
| Apr 19 | Final project report due (11pm) | |
| April 23 | Final exam |
Instructor: Mirek Riedewald
TA: We have no TA this semester :-(
Lecture times: Mon 6 - 9 PM
Lecture location: Ryder Hall 429
CS 5800 or CS 7800, or consent of instructor
Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011
Recommended books for further reading:
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