Data and Knowledge Engineering

Abstract

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.

Syllabus

Ensemble of trees for classification and knowledge discoveryParameterless clustering for knowledge discovery from dataData pre-processing and cleansing for data quality improvementPrivacy preserving data mining, publishing and sharingTime series data mining

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.

Assumed knowledge

Familiarity with data mining and visualisation concepts similar to the levels covered in ITC516 and ITC556

Enrolment restrictions

Only available to postgraduate students.