Singapore University of Social Sciences

Association and Clustering

Association and Clustering (ANL305)

Applications Open: 01 April 2020

Applications Close: 14 June 2020

Next Available Intake: July 2020

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: $1378 View More Details on Fees

Area of Interest: Business Administration

Schemes: Alumni Continuing Education + (ACE+), Lifelong Learning Credit (L2C), Resilience 2020

Funding: SkillsFuture, Union Training Assistance Programme (UTAP)

School/Department: School of Business


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
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