Lecture Notes on K-Nearest Neighbors (KNN) and Association Rule Mining

Introduction and Overview

• Lecture Date: [Specify Date]
• Lecturer's Status: Recovered from a non-emergency postponement confusion
• Participants: Engaged 26 students, discussions on group assignments, usage of AI transcription tools

K-Nearest Neighbors (KNN)

Recap of KNN

• KNN: Classification technique; can also perform regression
• Comparison with Decision Trees: Difference in defining 'nearest'
  • Decision Trees: Uses entropy minimization for splits
  • KNN: Uses distance measures

Distance Metrics

• Euclidean Distance: Primary focus for numeric data
• Importance of Standardization: To handle different scales (age as two digits, income as large five/seven-digit numbers)
• Scaling Techniques: Z-score standardization to make data unitless

Algorithm Details

• Choice of K: Commonly odd numbers (3, 5, 7, etc.) to avoid ties
• Distance Weighted KNN: Higher weight to closer points; common weights include the inverse of squared distance
• Overfitting in KNN:
  • High K Values: Helps in reducing overfitting
  • Removing Class Outliers: Strategies like Edited Nearest Neighbors

Issues and Solutions

• Dimensionality: High dimensions impact distances and relevance
  • Curse of Dimensionality: Sparsity increases with higher dimensions
  • Solutions: Dimensionality reduction (e.g., Principal Component Analysis, domain knowledge, feature selection)
• Large Data Sets: Computational complexity
  • Condensed KNN: Select prototypes using Hart's algorithm to reduce data size without impacting classification

Applications and Examples

• Anomaly Detection: Credit card fraud
  • Data Set Description: Highly imbalanced, with only a small fraction being fraudulent
  • Performance Metrics: Support, confidence, lift; precision, recall, F1-score, ROC curve
• Implementation Steps: Standardization, K selection, evaluation of metrics

Association Rule Mining

Introduction

• Unsupervised Learning: Looks for co-occurrence of items
• Terminology: Frequent itemset, antecedents, consequents

Measures for Evaluation

• Support: Fraction of transactions where both antecedents and consequents appear
• Confidence: Fraction of antecedent transactions also having the consequent
• Lift: Measure of the association's strength
  • Lift > 1: Positive association
  • Lift = 1: No association
  • Lift < 1: Negative association

Examples and Applications

• Market Basket Analysis: Classic example (e.g., beer and diapers myth)
• Other Domains: Medical diagnoses, online recommendations, fraud detection

Statistical and Business Filters for Rules

• Min Support and Min Confidence: Filtering irrelevant rules
• Real-life Examples: Pop-tart sales before hurricanes, consumer electronics purchasing behavior

Concluding Remarks

• Statistical Evaluation: Helps in reducing trivial, interesting but known, and inexplicable rules
• Next Steps: Discussing a priori and other algorithms in the next lecture

Questions and Interactions

• Addressing student questions on applicability, computational complexity, and methods for filtering results

Next Class Preparation

• Topics: Algorithms for Association Rule Mining (a priori, FP growth)
• Additional Reading: Research papers on the application of these rules in various domains