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Classification of Association Rules using WEKA

MS Data Mining – Practical Class

Rule-Based Classification

dr. Jamolbek Mattiev

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Practical Class Objectives

  • Understand rule-based classification
    • Learn the difference between association rules and classification rules
    • Use WEKA to build rule-based classifiers
    • Interpret and evaluate classification rules

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From Association Rules to Classification

  • Association rules discover relationships between items
    • Classification rules predict a class label
    • Rules take the form: IF condition THEN class
    • Subset of association rules used for prediction

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Rule-Based Classification

  • Classification using a set of IF–THEN rules
    • Each rule represents a decision pattern
    • Rules are human-readable
    • Common in knowledge-based systems

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Structure of Classification Rules

  • IF (attribute conditions) THEN class = C
    • Conditions can be numeric or categorical
    • Rules are evaluated sequentially
    • Default rule handles uncovered instances

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Rule-Based Algorithms in WEKA

  • JRip (RIPPER algorithm)
    • PART (Partial Decision Trees)
    • Decision Table
    • OneR (One Rule classifier)

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JRip (RIPPER)

  • Learns rules directly from data
    • Optimizes rules using error minimization
    • Produces compact rule sets
    • Suitable for classification tasks

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PART Classifier

  • Generates rules from partial decision trees
    • Combines decision trees and rule learning
    • No global optimization step
    • Easy-to-interpret rules

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Dataset Preparation

  • Dataset must contain a class attribute
    • Data stored in ARFF or CSV format
    • Preprocessing may be required
    • Nominal attributes work best for rules

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Steps in WEKA

  • Open WEKA Explorer
    • Load the dataset
    • Go to the 'Classify' tab
    • Select a rule-based classifier

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Running the Classifier

  • Choose JRip or PART
    • Set class attribute
    • Select evaluation method (cross-validation)
    • Click 'Start'

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Classifier Output

  • Generated classification rules
    • Rule coverage and errors
    • Confusion matrix
    • Overall accuracy

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Interpreting Classification Rules

  • Focus on high-coverage rules
    • Check misclassification errors
    • Avoid overfitting
    • Validate with domain knowledge

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Advantages and Limitations

  • Easy to understand and explain
    • Fast training time
    • May produce many rules
    • Less accurate on complex data

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Conclusion

  • Rule-based classification is interpretable
    • WEKA provides multiple rule learners
    • Useful for decision support systems
    • Best suited for structured datasets