Contents

• List of Figures
• Introduction
  • Our Task
  • Reader's Guide
• Data Mining
  • What is Data Mining
    • Training and Test Sets
  • Examples from Real Life
  • Learning
    • Inferring Information Using Deduction or Induction
    • Supervised and Unsupervised Learning
  • Representation of Knowledge
    • Propositional Representations
    • First Order Logic
    • Other Representations
  • Rule Generation
    • Semantics of Definite Rules
    • Non-Monotonic and Default Reasoning
  • Problems and Challenges in Data Mining
    • Noisy Data
    • Difficult Training Set
    • Databases are Dynamic
    • Databases may be Huge
  • Evaluation Criteria for Data Mining Systems
    • Cost of the Learning Set
    • Time and Memory Constraints During Learning
    • Correct Prediction
      • Prediction when the Training Set is Noisy
    • Efficiency of Learned Knowledge
    • Syntactic and Semantic Simplicity of Result
  • Methods for Data Mining
    • Bayesian Approach
    • ID3
    • Rough Sets Approach
• Rough Sets
  • Information System
  • Discerning Objects
    • Discernibility Matrix
    • Discernibility Functions
    • Reducing Representation
    • Upper and Lower Approximation
  • From Reducts to Rules
  • Rough Membership Function
• An Algorithm for Default Rules Generation
  • Definitions
  • A Motivating Example
  • The Algorithm
  • Traversal Example
• Classification
  • Methods for Using Existing Rules
    • Accuracy of the Rules:
    • Weight of Evidence Measure
      • Detection of Underlying Patterns
      • Rule Generation
      • Determination of Class Membership
  • Rules Available to the Classifier
    • Problems and Opportunities
      • Conflicting Rules
      • Rules in a Sub-Lattice
      • Multiple Equal Rules
  • The Implemented System
    • Testing or Classification of New Objects
    • Statistics
    • Validity Measure
    • Methods
      • First Rule
      • The Rule with Highest Accuracy Matching
      • Simple Voting
      • Voting by the Weight of Evidence Measure
    • Ignoring Blocks
    • Using Certain Attributes
    • Using Certain Rule bases
    • Possibility for Later Additions
• Results
• State of the Art and Related Work
  • The Data Mining Process
  • Specific Data Mining Applications
  • Rough Sets Applications
• Conclusions and Future Work
  • Summary of Important Features
  • Conclusion
  • Improvements to RGEN
  • Improvements to RCLASS
• References
• User's Guide
  • System Overview
  • Main Program
  • RGEN - Rules Generation
    • The User Interface
    • Running RGEN Directly from the Command Prompt
  • RCLASS - Classification
    • The User Interface
    • Running RCLASS Directly from the Command Prompt
• Experimenting with RGEN and RCLASS
  • Preparing for the Tests
  • The Experiments
    • Data From UCI Repository
    • Data From SCDS
    • Possible Tests not Performed
• File formats
  • Table File
  • Dictionary file
  • Rule File
    • The RGEN Rule File
    • Using RSES Rules in RCLASS
    • Using RGEN Rules in RSES
• Implementation Issues
  • Program Structure Overview
    • Internal List Format for Rules
    • Internal Format for the Dictionary
  • Classes
    • AVPair:public TObject
    • AttrMap
    • AttrMapList
    • AttrList
    • Block:public TObject, public ObjList
    • BlockList:public TObject, public ObjList
    • Classifier
    • Rule:public TObject, public ObjList
    • RuleList:public TObject, public ObjList
    • TDTable
• Changes Made to RGEN
  • Faster Reduct Calculation
  • Fixing the Dictionary Format
  • Making the Rules Look Like in RSES
  • Traversing the Lattice Without Calculating Rules
• Our Code
  • makefile
  • AVPair.h
  • AVPair.C
  • AttrList.h
  • AttrList.C
  • block.h
  • block.C
  • blocklst.h
  • blocklst.C
  • classifi.h
  • classifi.C
  • List.h
  • List.C
  • Minimize.h
  • Minimize.C
  • rclass.C
  • rule.h
  • rule.C
  • rulelist.C
  • rulelist.h
  • Code for the Script rgen2rses
  • Tk/Tcl code for the Graphical User Interface
    • Main program - file MAIN
    • RGEN user interface - file RGEN
    • RCLASS user interface - file RCLASS
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