CISC333 Data Mining: Fall 2013

2011 final exam

2010 final exam

Fall 2008 final exam

Winter 2008 final exam

2007 final exam

2006 final exam

2005 final exam



This course is offered in Fall 2013 in slot 2 in Ontario 207. The prerequisites are CISC121 and a stats course. Although the course is numbered at 3rd Year, second-year students who wish to take it may do so (although this was more likely when it was offered in the Winter Term).

Course Material

This course is available on Moodle, so look for most of the information there.


The tool we will use for most of the practical work in the course is Rapidminer, a development from the Weka toolkit. You can find extensive tutorial material on the Rapid website.

Rapidminer is available for free download at; you need Rapidminer Community Edition (should be Rapidminer 5).

Rapidminer is also available on the Caslab machines, under either Windows or Linux.

Tutorial on using Matlab (useful for visualization) and the SVD and ICA matrix decompositions.

Exercises and Assignments

Exercises are a chance for you to get some hands-on experience. The exercise questions will often be open-ended. You might expect to spend 3 or 4 hours on these sheets each week. Each one is marked on this scale: acceptable; inadequate; or not seriously attempted. There will be five exercise sheets in the first half of the term.

(Exercise sheets will appear a week or ten days before they are due.)


This table describes what we will cover, keyed to the modules and text.

Avail means that the basic powerpoint is available. Done means that the marked-up powerpoint is available.


Tan, Steinbach, Kumar, Introduction to Data Mining, Addison-Wesley, 2006, ISBN 0-321-32136-7 ($105 at Amazon, $97.95 at Campus Bookstore).


There are 3 deliverables:

  1. 5 weekly exercise sheets, due in each of weeks 2-6. These exercise sheets are not marked as such. However, for each exercise sheet that is not adequate, your final mark in the course will be reduced by a factor of 0.02. The exercise sheets are useful both for understanding the material and as ground work for the project, so they are worth doing!
  2. A project in which you can apply data mining techniques to a real dataset and report on what you discover, due at the end of week 11, and worth 30% of your final grade.
  3. A final examination, worth the remaining 70% of your grade. You must pass the exam to pass the course. You may bring one 8.5x11 sheet of notes into the exam.

Note that the assessment in the course is backloaded, so please take this into account when planning your procrastination.

Your instruction team


David Skillicorn
528 Goodwin Hall
skill cs queensu ca
533 6065

Questions? Try asking me before or after class, or come and find me at my office any time I'm there. I have a schedule posted by my door.

I will schedule an office hour once term has started.

Teaching Assistant


Study skills. You probably know all of the conventional wisdom about how to learn, but perhaps you don't actually use it. Here is an excellent link: Study Hacks.

You may also want to subscribe to, or read: KD Nuggets

ModuleContentText Refs
Module 0IntroductionChapter 1
Module 1Prediction and ExplorationBits of Chapter 2
Module 2Data preparation and model qualityChapter 2
Module 3Simple predictors
Module 4Decision treesChapter 4
Module 5More decision trees
Module 6Neural networksChapter 5.4
Module 7Support Vector MachinesChapter 5.5
Module 8Rule based systemsChapter 5.1
Module 9Object selection: sampling, ensemble techniquesChapter 5.6
Module 10Attribute selectionSection 2.3.4
Module 11Prediction case studies
Module 12Clustering I: similarities, k-meansChapter 8.2
Module 13Clustering II: Expectation-MaximizationSection 9.2.2
Module 14Clustering III: Top-down and bottom-up clusteringChapter 8.3
Module 15Clustering IV: Matrix decompositions
Module 16Clustering V: Clustering Large DatasetsSection 9.5.2,Section 8.4.2
Module 17VisualizationChapter 3.3
Module 18Clustering Case Study
Module 19Clustering VI: Biclustering
Module 20Biclustering Case Study: Topic Detection
Module 21Mining the Web
Module 22Collaborative Filtering
Module 23Adversarial Data Mining
Module 24Summary