Transcript Document

Summary
„Rough sets and Data mining”
Vietnam national university in Hanoi,
College of technology, Feb.2006
Main topics:
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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
Classification methods
Boolean reasoning methodology
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Monotone Boolean function
Implicant, prime implicant
Searching for minimal prime implicants of a
monotone Boolean function
Data preprocessing and data cleaning
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Discretization methods
Data reduction methods
Missing values
Outlier elimination
Rough set methods for discretization and attribute
reduction
Association rules
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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 and rough set
methods
Classification methods
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Instance-based classification techniques
Bayesian classifiers
Decision tree methods
Decision rules methods
Classifier evaluation techniques
Discernibility measure
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Applications of discernibility measure in
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Feature selection
Discretization
Symbolic value grouping
Decision tree construction