Tài liệu của thầy Nguyễn Hùng Sơn:
http://www.mimuw.edu.pl/~son/datamining/datamining.htm
Các thuật toán của Data Mining và ứng dụng của lý thuyết tập thô trong Data Mining.

Data mining

1. Introduction to KDD and data mining; templates and patterns
2. Transaction data analysis and association rules; main algorithms for association rule generation: Apriori, AprioriTid, FP-tree.
3. Classification problem, case based methods, naïve Bayes classifiers, Bayesian networks.
4. Data cleaning and data preprocessing techniques;
5. Decision trees;
6. Rule based classifiers;
7. Classifier evaluation methods;
8. Clustering problem and clustering algorithms
9. Searching for sequence patterns from time series data
10. Summary

Rough sets in Data Mining

1. Introduction: Data mining and rough set theory (RSDM1)
2. Boolean reasoning approach to rough set and data mining (RSDM2)
3. Rough sets approach to feature selection problem & decision rules (DM6 and RSDM2)
4. Rough sets and association rules (DM2 and RSDM2)
5. Rough sets in data preprocessing (discretization of real value attributes)(RSDM3 and DM4)
6. Computational learning theorem (RSDM4)
7. Rough sets in decision trees and rough set methods for large data sets (RSDM5 and DM5)
8. Rough sets and layered learning.
9. Summary

Phần mềm RSES (Rough Set Exploration System): http://logic.mimuw.edu.pl/~rses/

RSES is a toolkit for analysis of table data running under Windows NT/95/98/2000/XP. It is based on methods and algorithms coming from the area of Rough Sets. It comprises of two general components - the GUI front-end and the computational kernel. The kernel is based on renewed RSESlib  library.