Association and Clustering

Synopsis

ANL305 Association and Clustering equips students with the skills and knowledge in applying Association Rule Mining, Clustering, as well as related unsupervised learning techniques. The course covers general approaches to developing analytics solutions when confronted with real-world problems. Students will learn how to address the problems using the techniques taught in this course. Last but not least, students will also be exposed to applying unsupervised data exploration and analysis using cloud services.

Level: 3
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

Introduction to Association and Clustering
Basic Unsupervised Learning: Nearest Neighbours and Dimension Reduction
Data Exploration and Preparation for Rule Mining
Association Rule Mining: Apriori and CARMA
Visualization of Association Rules
Sequence Pattern Mining
Data Exploration and Preparation for Clustering
Partitional Clustering
Hierarchical Clustering
Self-Organising Map
Two-Step Clustering
Applying Unsupervised Learning on Cloud Services

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

Singapore University of Social Sciences

Association and Clustering