SCHEDULE & GRADING

 

Schedule
Introduction	week 2
Data Warehouse and OLAP	week 3
Data Preparation	week 4
Assignment 1 (MSSQL OLAP) -- Due. Mar. 19	week 4
DMQL & DBMiner Presentation Proposal -- Due Mar. 26	week 5
Characterization and Discrimination 李仁皓	week 6
Assignment 2 (DBMiner) -- Due. Apr. 9	week 7
Association Rule 楊士賢, 徐龍凱	week 8
Assignment 3 (Association Rule) -- Due. Apr. 30 (postponed to May 7)	week 9
Classification 吳東軒, 張永佳, 林建文	week 10
Clustering 張毓美, 吳鴻俊, 楊璿	week 11
Case Study 王士賢, 鄧德華, 黃世承, 劉奕酉, 駱德堯	week 12
Exam, May. 14 -- 30%	week 13
Term Project -- Presentation order	3 weeks
Others -- 10% [Selected or Voluntary] Assignment presentation Oral presentation topics: Bibliographic notes in each chapter: Statistics: multiple variable analysis, multi-dimentional discretization Association Rules: quantitative association rules, visualization Classification: bayesian belief networks, neural network, fuzzy set Clustering: density-based, grid-based, model-based methods, etc. Chapter 5: attribute relevance, attribute set selection Chapter 3: multidimensional index trees, automatic discretization Chpater 2: discovery-oriented data cubes

TERM PROJECT

You have two options for the term project in this course: implementation-based and application-based. You can, based on your preference, choose either one. They have the same weight (30%) in your final grade.

Option I: Implmentation-based Project

There are three typical kinds of knowledge in data mining. They can be described as:

• Classification
• Association
• Clustering

You are required to choose any one of these algorithms and implement it using C++ or C, and test your program using some real data which can be obtained  from University of California-Irvine: Machine Learning Database Repositories. Please spend some time navigating through the different data sets there and  select the most suitable one for your testing. You might select several others to test your program to show your program does work.

You are required to prepare a documentation of your project, including the description of your project, the algorithm, design diagram, key data structures, source code, and  the testing results (input/output).  You need to explain your test and test results, including any references to help people understand the significance and interestingness of your work.

Option II: Application-based Project

The students are asked to choose one application domain, and prepare the documentation for your case study including:

1. The application case.
2. How do you prepare for your data?
3. Choose the mining type
4. How would you explain your result?
5. What problems you might encounter?

Note: The documentation should be printed using in 12pt font, single line spacing, and should not exceed 15 pages. Please also prepare 30-minute slides to present your work. The length of the essay, though not strictly required, should be between 10 to 15 pages. However, we pay more attention to the quality of your essay, not just the number of pages. Test data can be download from Datasets for Machine Learning, Knowledge Discovery and Data Mining or PKDD Cup.