ITC573 Data and Knowledge Engineering (8)

The subject provides students with in-depth study of data and knowledge engineering and their use in real life business. It looks into interpreting data through advanced approaches such as an ensemble of trees and clustering. Given the importance of clean and useful data for knowledge discovery, it offers thorough discussion on data pre-processing tasks including missing value imputation, corrupt data detection, discretization, and feature selection. The subject offers a study of the preservation of privacy when data mining, publishing and sharing among business organisations. It uses the current tools for knowledge discovery and future prediction.


Session 2 (60)
Bathurst Campus

Continuing students should consult the SAL for current offering details: ITC573. Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System



One session


School of Computing and Mathematics

Enrolment Restrictions

Only available to postgraduate students.

Assumed Knowledge

ITC516 Data Mining and Visualisation or equivalent.

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to compare and evaluate various knowledge discovery techniques;
  • be able to identify and design approaches for knowledge discovery from data for making critical business decision;
  • be able to compare and critique various data pre-processing techniques;
  • be able to evaluate the usefulness of data cleansing and pre-processing in discovering useful knowledge necessary for critical business decision;
  • be able to critically analyse privacy preservation in data mining, data publishing and data sharing; and
  • be able to evaluate and compare time series data mining approaches for business decision making.


This subject will cover the following topics:
  • Ensemble of trees for classification and knowledge discovery
  • Parameterless clustering for knowledge discovery from data
  • Data pre-processing and cleansing for data quality improvement
  • Privacy preserving data mining, publishing and sharing
  • Time series data mining


For further information about courses and subjects outlined in the CSU handbook please contact:

Current students

Future students

The information contained in the CSU Handbook was accurate at the date of publication: May 2019. The University reserves the right to vary the information at any time without notice.