MCS314 ( Diwali Semester
2009) Special Topics in Data Mining
Advanced
Classification Techniques (4
credit course : 3-0-2)
Being an advanced course in Data Mining, the pre-requisite is MCS104. Other pre-requisite is a course on Algorithms and fair understanding of statistics and linear algebra. Strong programming skills and dexterity with complex data structures will be of advantage.
Tentative Weekly schedule
Week |
Topics |
Suggested Tasks |
Week 1 |
Recapitulate Classification,,
Taxonomy of Classification Methods, Decision Functions and Notation building |
Download C4.5 and get familiarized
Revise scripting language |
Week 2 - 3 |
Evaluation of Classifiers, No
Free Lunch theorem |
Use gnuplot to draw graphs, Check Assignment 1 |
Week 4 - 5 |
Decision Trees : Splitting
criteria, Pruning, Induction Algorithms |
Carry on with the assignment. All
the best!! |
Week 6- 9 |
Support Vector Machines |
Download an SVM implementation and
get familiarized Check Assignment 2 |
Week 10-12 |
Classification Ensembles |
Check Assignment 3 |
Week 13-14 |
Hybrid Classifiers |
Good luck with the assignment |
Books:
Supporting Texts
Research Papers
(To be announced)
Internal Assessment
There will be three programming assignments (10 marks each) for the course to be done in the group of two. The deadlines are strict.
2 Minors as per the schedule displayed on the department notice board (15 marks each)
Syllabus for Minor 1 : Portion of the
syllabus covered up to week 6.
Syllabus for Minor 2 : Syllabus covered
between week 7 - 12
Assignment 1 (Submission date 1 Sept 2009)
Write a script/program for cross-validation. Use a dataset from UCI ML repository and C4.5 classifier with default parameters and do the following:
i) for each fold compute precision, recall, specificity and f-measure, plot graph (measure vs. fold)
ii) compute cross validation measures for different values of k and plot graph (measure vs. k)
iii) Draw ROC curve by varying the pruning confidence level
iv) Write a report containing the three graphs and your observations
Assignment 2: As announced in class. Submission deadline Sept 28th
Assignment 3: As announced in class. Evaluation to take place in lab on Oct 27th.