Course Code: ANL305
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Synopsis

Association and Clustering (ANL305) equips students with key skills in Association Rule Mining, Clustering, and other unsupervised learning techniques. The course covers methods for developing unsupervised analytics solutions to address real-world problems. Students will learn how to apply techniques such as various clustering and association rule mining algorithms to uncover patterns and insights from data. By the end of the course, students will be able to use Python for unsupervised data analysis and apply these techniques to solve real-world problems across various industries.
Level: 3
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

  • Introduction to Association
  • Data Preparation for Association Analysis
  • Association Rule Mining: Apriori
  • Visualisation of Association Rules
  • Introduction to Clustering
  • Data Preparation for Clustering
  • Proximity Measure
  • Dimension Reduction
  • Partitional Clustering
  • Hierarchical Clustering
  • Density-based spatial clustering of applications with noise (DBSCAN)
  • Anomaly Detection – Local Outlier Factor

Learning Outcome

  • Discuss various conceptual or practical aspects of applying association and clustering
  • Appraise the application of an unsupervised learning method in a given context
  • Compare different techniques for association and clustering
  • Construct association or clustering models using appropriate software
  • Evaluate the performance of unsupervised learning models
  • Analyse and interpret the results of association and clustering
Full-time Programmes Part-time Programmes / CET Courses