Lecture |
Subject |
Material |
References |
1 |
General information about the course |
slides |
- |
2 |
Introduction to data mining |
slides |
Textbook (1) - chapter 1, Textbook (2) - chapter 1 |
3 |
Statistical descriptions of data, Some useful inequalities |
|
Textbook (1) - chapter 2, Sushant Sachdeva's notes on concentration bounds pdf |
4 |
Finding similar items, Distance measures, minHash |
|
Textbook (2) - chapter 3 |
5 |
minHash, Locality Sensitive Hashing (LSH) |
|
Textbook (2) - chapter 3 |
6 |
LSH families of functions |
|
Textbook (2) - chapter 3 |
7 |
Clustering, K-means algorithm |
|
Textbook (1) - chapter 10 |
8 |
K-means algorithm, K-center Clustering |
|
Textbook (1) - chapter 10, Sanjoy Dasgupta's notes pdf,
Michael Dinitz's notes pdf |
9 |
K-Medoid clustering, Hierarchical Clustering |
|
Textbook (1) - chapter 10 |
10 |
Incremental Clustering |
|
Incremental Clustering and Dynamic Information Retrieval pdf |
11 |
Probabilistic Clustering, Maximum Likelihood |
|
Ryan Martin's notes pdf. Songfeng Zheng's notes pdf |
12 |
Mixture Model, Expectation-Maximization |
|
Textbook (1) - chapter 11, Alexandra Chouldechova's slides pdf |
13 |
Spectral Clustering |
|
Textbook (1) - chapter 11, A tutorial on spectral clustering by Ulrike von Luxburg pdf |
14 |
Spectral Clustering, Continued |
|
Textbook (1) - chapter 11, A tutorial on spectral clustering by Ulrike von Luxburg pdf |
15 |
Clustering Overview, Clustering Evaluation |
|
Textbook (1) - chapter 10, Slides by Michael Hahsler pdf |
16 |
Outlier Detection I |
|
Textbook (1) - chapter 12 |
17 |
Outlier Detection II |
|
Textbook (1) - chapter 12 |
18 |
Association Rules, Frequent Itemsets, Apriori Algorithm |
|
Textbook (1) - chapter 6 |
19 |
Apriori Algorithm, Data Streams, Majority Algorithm |
|
Textbook (1) - chapter 6, paper by G. Cormode and M. Hadjieleftheriou pdf |
20 |
Space-Saving Algorithm, Count-Min Sketch |
|
G. Cormode and M. Hadjieleftheriou pdf, Metwally et al pdf |