|
CS423: Data Mining (1/2017)Class meeting: 9.30-11.00 Tuesday,Friday @ CSB 202 Lecturer: Jakramate Bootkrajang Email: jakramate.b@cmu.ac.th Office hours : 17.00-18.00 Tuesday,Friday @ CSB 107 |
- Class marks (Quiz and HWs) (as of 10 Nov) [click]
- Due to numerical instability, dataset for HW2 is changed from 'biodeg.mat' to 'boston.mat'
- คะแนนกลางภาค [click]
- การสอบกลางภาค: 6 ตุลาคม 2560 เวลา 12.00-15.00 ห้องสอบ CSB 209
- Good news! The deadline for assignment #1 has been extended to 24 Sep.
- Welcome to course's homepage
- New slides for Ch03-Preprocessing uploaded
- I. Introduction + Basic concepts
- II. Data preprocessing
- Data cleaning
- Data integration
- Data transformation
- III. Dimensionality Reduction Techniques
- PCA
- Feature subset selection
- Random projection
- VI. Mining Association Rules and Recommendation system
- Association rule mining
- Recommendation systems
- V. Classification
- Bayesian Learning
- Linear Discriminant Analysis
- Logistic Regression
- K-nearest neighbours
- Classifier evaluation
- VI. Clustering
- Partitioning clustering -- k-means, k-medoids
- Hierarchical clustering -- agglomerative, divisive
Class schedule
| Date | Lecture (Tue) | Date | Lecture (Fri) |
| 8 Aug | First hour | 11 Aug | Introduction [Slides] |
| 15 Aug | Basic concept 1 [Slides] | 18 Aug | สัปดาห์วันวิทยาศาสตร์ |
| 22 Aug | Basic concept 2 | 25 Aug | Data preprocessing 1 [Slides] |
| 29 Aug | Data preprocessing 2 Supplementary reading | 1 Sep | Dimensionality reduction (PCA) [Slides] |
| 5 Sep | Dimensionality reduction (PCA) [Slides] | 8 Sep | PCA Lab [Slides] [Dataset] |
| 12 Sep | Feature subset selection and Random projection [Slides] | 15 Sep | Association rules [Slides] [Original paper] |
| 19 Sep | Content-based recommendation [Slides] | 22 Sep | Collaborative filtering [Slides] |
| 26 Sep | Recommendation system lab | 29 Sep | ทบทวนก่อนสอบ |
| 3 Oct | สอบกลางภาค | 6 Oct | สอบกลางภาค |
| 10 Oct | Bayesian learning 1 [Slides] | 13 Oct | วันหยุด |
| 17 Oct | Bayesian learning 2 [Slides] | 20 Oct | Linear Discriminant [Slides] |
| 24 Oct | Discriminant Analysis [Slides] | 27 Oct | Logistic regression [Slides] |
| 30 Oct | No class | 2 Nov | KNN [Slides] |
| 6 Nov | Logistic Regression Lab [data] | 9 Nov | Classifier evaluation [Slides] |
| 13 Nov | k-Means [Slides] | 16 Nov | k-Medoids |
| 20 Nov | Hierarchical clustering 1 [Slides] | 23 | Hierarchical clustering 2 |
| 27 Nov | สอบปลายภาค | 30 Nov | สอบปลายภาค |
Assigments
- Assignment 1 Instruction Files (Due 19 Sep 2017, Extended to 24 Sep)
- Assignment 2 Instruction Files (Due 7 Nov 2017)
- Assignment 3 Instruction Datasets (Due 26 Nov 2017)
- Assignment 4 Instruction (Due 4 Dec 2017)
Grading
- Midterm 20 %
- Final 30 %
- Assignments 4 x 10 = 40 %
- Quiz 10 %
Useful resources
- Books
- Data Mining, Concepts and Techniques: Jiawei Han and Micheline Kamber, MORGAN KAUFMANN.
- Data Mining, Concepts, Models, Methods, and Algorithms: Mehmed Kantardzic, WILEY-INTERSCIENCE
- Pattern Classification: Peter E. Hart, David G. Stork, and Richard O. Duda, WILEY
- Mining From Massive Data: Anand Rajaraman, Jure Leskovec, Jeffrey D. Ullman [PDF]
- Lecture note (in Thai)
- Data mining [PDF]
- Papars
- I.Guyon and A. Elisseeff, An Introduction to Variable and Feature Selection, JMLR (2003)
- CC.Aggarwal, A.Hinneburg, DA. Keim, On the Surprising Behavior of Distance Metrics in High Dimensional Space, ICDT (2001)
- Online Material
- Julia language
- Julia language quick introduction
- Distance measures summarisation