Time and Venue: MW 10:30PM - 11:50AM, Rm 5583
Lei Chen (send e-mail for questions regarding the class and for arranging individual meetings)
Zheng Liu (zliual@cse.ust.hk)
Data mining has emerged as a major frontier field of study in recent years. Aimed at extracting useful and interesting patterns and knowledge from large data repositories such as databases and the Web, the field of data mining integrates techniques from database, statistics and artificial intelligence. This course will provide a broad overview of the field, preparing the students with the ability to conduct research in the field.
Project Presentation Score Bonus Marks
1. Project Presentation Scores will be given by the audience (5%) and the instructor (5%)
2. Participant and Marking Bonus: all the students are strongly encouraged to attend project presentation sessions and give marks to the presenter (the score sheet will be distributed at the beginning of the session). I will give you bonus 0.25 mark for each filled score sheet.
3. Question Bonus: all the students are encouraged to ask questions during the Question/Answer session after each presentation. Each student is allowed to ask one question in each paper presentation. For each asked question, I will give you bonus 0.5 mark. You can staple your bonus coupon which we will give you on your filled score sheet.
Please note, the questions like “Can you explain more?”, “I cannot understand, can you repeat?” will not be counted. For each paper presentation’s Q/A session, at most 3 questions are allowed to ask.
Project
Please check the project page.
Important Dates:
News:
Welcome to COMP5331
Group information confirmed.
Presentation Signup Sheet (https://docs.google.com/spreadsheets/d/1c_anxZWYpXNmK18qiigqv413RgMqCJSzXLUmlQrhSnQ/edit?usp=sharing)
Midterm Exam: Oct 12th
10:30am-12:00pm
Final Exam: 16 December 2016 16:30 -19:30 CYTG010
Tentative
Schedule
|
Date |
Le cture Slides |
Text book |
Remarks |
|
M, Sept 5th |
HK, Chpater 1 |
|
|
|
M, Sept 12th |
Mining Frequent Patterns,
Associations and Correlations: Basic Concepts and Methods (PPT, PDF) |
TSK, Chapter 6 HK, Chapter 6 |
|
|
M, Sept 19th |
Advanced Frequent Pattern
Mining (PPT, PDF) Guest
Lecture by Prof. Yangqiu Song Incorporating Structured World
Knowledge into Unstructured Text via Heterogeneous Information Networks (PPT, PDF) |
TSK, Chapter 7 HK, Chapter 7 |
|
|
M, Sept 26th |
HK, Chapter 9 |
||
|
M, Oct 3rd |
HK, Chapter 10 |
||
|
W, Oct 12th |
Mid-term Exam (in Class) |
HK, Chapter 10 |
|
|
M, Oct 17th |
HK, Chapter 11 |
|
|
|
M, Oct 24th |
|
||
|
M, Oct 31st |
Outlier Analysis (PPT,
PDF) LOF example
(PDF) |
TSK, Chapter 10 HK, Chapter 12 |
LOF Example |
|
M Nov.7th |
Introduction to
Web Data Mining (PPT, PDF) Final Review (PPT) |
HK, Chapter 4, and 5 |
|
|
M, Nov. 14th |
Project Presentation Mining Approximate TopK Non-redundant Rules (PPT) Exploring the Skyline Pattern
Mining (PPT) |
Group 11 (LEUNG Chung Yin) Group 12 (Wu Ziming, Sun Mingfei) |
|
|
M, Nov 21th |
Project
Presentation Clustering Graph Streams (PPT) Discovering Relations between Culture and Personal Background
based on OkCupid Database (PPT) Given Word representation to Rare Word (PPT) Batch
incremental mining with loose FP-Tree and FP-growth (PPT) A study
on Novel Recommendation based on Personal Popularity Tendency (PDF) Mining
Utility Functions (PPT) |
Presentation |
|
|
M, Nov. 28th |
Recommender Systems with
Trust-based Social Networks (PDF) Discovering
sentiments by text mining and statistical analysis of media review (PPT) UP-Growth (PPT) Food
recommendation using personal popularity tendency (PPT) The
Comparison of Recommendation Approaches in Movielens
(PDF) Discovering
Relations between Culture and Personal Background based on OkCupid Database (PPT) |
Presentation |
|