CIS338: Data Mining 

Week 1 (6 Oct - )

Course presentation and Data Mining applications

Data Mining: Introduction pdf
Read Chapter 1 of Roiger's Data Mining book, completed with Han's Data Mining book, see reading list below

No lab

Optional Homework: Search for the entry Data Mining in Wikipedia, and read about its notable uses.

Week 2 (13 Oct - )

Data Mining introductory concepts, and the k Nearest Neighbour algorithm (continued from week 1)

Lab: Code in Java an Automatic Diagnosing System based on Nearest Neighbour and 3 Nearest Neighbour algorithms

Optional Homework: Explore KDnuggets, one of the most popular websites with various information and resources related to Data Mining (see in particular DM software). 

Week 3 (20 Oct - )

Data Mining Strategies pdf
Read Chapter 2 

Lab: Finish your Java coding from the previous week

Optional Homework: Explore profiles of jobs in Data Mining on KDnuggets website.

Week 4 (27 Oct - )

Data Mining Techniques pdf
Read Chapter 3 (Section 3.4: Genetic Learning, is optional)

Lab: Code in Java an Automatic Diagnosing System based on the k Nearest Neighbour algorithm

Optional Homework: Read about Text Mining.

Week 5 (3 Nov - )

Data Mining Techniques (continued from previous week) [+ demo with Weka using tutorial]

Lab: Java Data Mining – finish your coding from the previous lab sessions.

Optional Homework:  Find a particular dataset that may present an interest for you to mine in the UCI KDD dataset repository. (The datasets are classified with respect to the practical problems to solve they have been created for)

Week 6 (10 Nov - )

Reading week

Week 7 (17 Nov - )

Data Mining Techniques (continued from previous week) [ + demo with Weka on association analysis with the dataset CCP_associations.csv and clustering with the datasets  kmeans.arff and numeric_dataset_cluster.csv]

Lab: Applications of Weka's supervised learning algorithms in medical diagnosing and medical research

Optional Homework: Read about SAS Enterprise Miner (see additional resources entry below)

Week 8 (24 Nov - )

Knowledge Discovery in Databases  pdf
Read Chapter 5

Lab: Perform Association analysis with Weka (allocate 5 min), see an online demo for k-means (allocate 5 min), and perform a clustering and assess clustering quality with Weka (allocate 40 min)

Optional Homework: Read about IBM SPSS Modeler (formerly Clementine, see additional resources entry below).

Week 9 (1 Dec - )

Data Mining with Neural Networks pdf [+ demo with Weka's Backpropagation algorithm on Feed-Forward Neural Nets with telecomservice dataset for churner prediction, and Portuguese wine dataset for wine quality estimation]
Read Chapter 8 (Section 8.5 is optional)

Lab: Data Mining application in Customer Analytics (Customer Churn/Defection/Attrition)

Optional Homework: Read about Bioinformatics and the application of Data Mining & Machine Learning in this area

Week 10 (8 Dec - )

Statistical Techniques pdf [bring laptops with you for practice in class on Linear Regression - Office buildings dataset (output: value) and Portuguese wine dataset (output: quality); estimation/prediction using Regression Trees - Portuguese wine dataset (output: quality); Logistic regression - Credit card promotions dataset (output: LIPromotion) and Breast cancer dataset (output: Class); Naive Bayesian classification - Credit card promotions dataset (output attribute: Sex)]
Read Chapter 10 (Expectation Maximisation Clustering and Conceptual Clustering are optional)

Lab:  Finish work from the previous week

Optional Homework: Read about Music Data Mining. See in this paper how Data Mining, in particular Weka with the C4.5 decision tree building algorithm (J48), could be used in the Automatic Music Classification problem.

Week 11 (15 Dec - )

Data Warehousing  pdf
Read Chapter 6 (Sections 6.1 and 6.4 are optional)

Seminar

Optional Homework: Explore this website with useful/practical information about Data Warehousing

Past exams

Recent exam papers are available here; See student intranet for previous papers.

Revision week

Production rules and classifier evaluation