Overview

This unit introduces the principles and techniques used by data miners. Data mining is used to add value to large collections of data, delivering discoveries that continue to revolutionarise lives in our data-rich but knowledge-hungry world.

Requisites

Prerequisites
STA70002 Multivariate Statistics
Teaching Periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date

Learning outcomes

Students who successfully complete this unit will be able to:

  • Formulate an understanding of the basic theory and principles of data mining
  • Assess prediction and classification methods such as regression, neural networks and decision trees, understanding how to choose between these methods
  • Evaluate unsupervised data mining methods and determine when these methods should be combined with supervised methods
  • Design ontologies based on text and creative visualisations of textual relationships
  • Report on the results of specific data mining projects

Teaching methods

Hawthorn Online

Type Hours per week Number of weeks Total (number of hours)
Online
Directed Online Learning and Independent Learning		12.50 12 weeks 150
TOTAL150

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Face to Face Contact (Phasing out)
Seminar		3.00 12 weeks 36
Specified Learning Activities (Phasing out)
Various		2.00 12 weeks 24
Unspecified Learning Activities (Phasing out)
Independent Learning		7.50 12 weeks 90
TOTAL150

Assessment

Type Task Weighting ULO's
Assignment 1Individual 20% 3,4,5 
Assignment 2Individual 20% 3,4,5 
ExaminationIndividual 50% 2,3,5 
Online QuizIndividual 10% 1,2 

Content

  • Introduction to data mining and a data mining package
  • Linear Models
  • Classification and regression trees
  • Random forests and boosting
  • Neural networks for classification and prediction
  • Self-organising maps
  • Cluster analysis
  • Memory Based Reasoning
  • Support Vector Machines
  • Text Mining

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.