School of Information Technologies
COMP5318 KNOWLEDGE DISCOVERY
Semester 1, 2013
Sanjay Chawla- course coordinator, lecturer and
Didi Surian - tutor
Fei Wang - tutor
Email: fwan7956 AT uni.sydney.edu.au
||Architecture LT 1
(start in Week 2)
||SIT labs 115, 116 and 117
Assessment overview site.
The assignment specifications will be available on the eLearning
class, Monday 15 April 2013
the 1st first hour of the lectures (6-7pm). Semi-open as the exam.
Students are allowed 1 sheet of their own notes (A4-size,
double-sided, handwritten or typed). The test will cover the material
possible to re-sit the test.
|| May 19th. 11:59 PM
||Individual or in groups (max 4 per group)||Submission:
electronically via eLearning
Instructions to hand in assignments
Room allocation for group presentation
penalty of minus 1 mark per each day after the deadline
- the maximum delay is 7 days; after that assignments will not be accepted
Research paper presentation
||Groups and Paper Assignment||- No late presentations are allowed; a student who is unable to present on the specified date will receive 0 marks for this assessment|
|Written exam|| 50
||The exam will be semi-open. You are allowed 1 sheet of your own notes (hand-written or typed, double-sided, A4-size) and a non-programable calculator (you don't need a calculator). No other material is allowed (no book, no additional notes). The exam will be on all material except Clustering.|
Academic honesty: Please read
Honesty and submit the appropriate cover sheet
with your signature with your assignments. The cover sheets are
available from the link above.
he teaching materials (
lecture notes, lab notes and lab solutions) will be available on the eLearning
Machine Learning and Knowledge Discovery in Databases; DM tasks.
similarity measures. Slides
Introduction to Map-Reduce.
Introduction to Clustering
Clustering and Probability
Codes for Tutorial
Mid Term Exam Solution
Association rules Tutorial
Classification based on Association Rules
Dimensionality Reduction Continued + Discussion on Assignment
Mining of Massive Data Sets
Anand Rajaram, Jure Leskovec and Jeff Ullman
Cambridge University Press
Pang-Ning Tan, Michael Steinbach, Vipin Kumar,
Pearson Education (Addison Wesley), 0-321-32136-7, 2006
Chapters 4, 6 and 8 are freely available here and from the publisher.
| Data mining - practical machine
tools and techniques with Java implementations, 3d edition
Ian H. Witten, Eibe Frank and M. Hall
Morgan Kaufmann, 2011, ISBN: 978-0-12-374856-0
Machine Learning view of Data Mining. Very readable. The book of the WEKA software. You cana lso use the previous edition of the book (2d edition).
Other recommended books
|Data Mining: Introductory and
Margaret Dunham, Prentice Hall, 0-13088892-3, 2003
Good coverage of the topics included in the course. Very readable. Pseudo code and computation complexity covered.
| Data Mining
Concepts and Techniques.
J. Han and M. Kamber
Morgan Kaufmann, 2006, ISBN 1-55860-901-6
Database view of Data Mining.
| Principles of Data
D. Hand, H. Mannila, P. Smyth, Principles of data mining,
MIT Press, 2001, ISBN: 0-262-08290-X
Statistical view of Data Mining. Advanced, requires good statistical knowledge.
Tan and Witten are placed in the library Reserve collection (2 Hour Loan collection) and are also available in the Co-op Bookshop.
Last modified: 12 May 2012