Summary
„Rough sets and Data mining”
Vietnam national university in Hanoi,
Main topics:
Definition, principles and functionalities of
data mining systems
Rough sets methodology to concept
approximation and data mining
Boolean reasoning approach to problem
solving
Data preprocessing and data cleaning
methods
Association rules
Boolean reasoning
methodology
Monotone Boolean function Implicant, prime implicant
Searching for minimal prime implicants
Data preprocessing and data
cleaning
Discretization methods Data reduction methods Missing values
Outlier elimination
Rough set methods for discretization
Association rules
Definition, possible applications
Apriori search for frequent patterns
and association rules
Modifications of apriori algorithms:
hash tree, Apriori-Tid, Apriori-Hybrid
FP-tree method
Relationship between association rule
Classification methods
Instance-based classification
techniques
Bayesian classifiers
Decision tree methods Decision rules methods
Discernibility measure
Applications of discernibility measure
in
Feature selection Discretization
Symbolic value grouping Decision tree construction
