Subject Descriptions - Subject Information


Calendar: 2017 Postgraduate
Faculty: Faculty of Engineering and Information Sciences
Department: School of Computing and Information Technology


Subject Information
Subject Code INFO911
Subject Name Data Mining and Knowledge Discovery
Credit Points 6
Pre-Requisites None.
Co-Requisites None.
Restrictions None.
Equivalence INFO411
Assessment  
General Subject No.
EFTSL (Non Weighted) 0.125
Non Weighted Student Contribution Amounts
Commonwealth Supported (HECS) Students Only
Pre-1997 Pre-2005 Post-2005 Post-2008 Post-2009 Post-2010
$ 1131  $ 1131  $ 1131  $ 1131  $ 1131  $ 1131 
Weighted Student Contribution Amounts  
Work Experience No
Tutorial Enrolment Information None.

Subject Availability
Session Autumn  (27-02-2017 to 22-06-2017)
Campus Wollongong
Delivery Method On Campus
Instance Name Class 1
Course Restrictions No restrictions
Contact Hours 2hrs lecture , 2hr lab
Lecturer(s) and
Cons. times
Guoxin Su
Markus Hagenbuchner
Robert Clark
Coordinator(s) and
Cons. times
Guoxin Su
Markus Hagenbuchner
Robert Clark
Instance Comment  
Census Date 31-03-2017

Subject Description
Introduction to Data Mining, Knowledge Discovery, and Big Data with coverage of Data Structures, role of Data Quality and per-processing, Association Rules, Artificial Neural Networks, Support Vector methods, Tree Based Methods, Clustering and Classification Methods, Regression and Statistical Methods, Overfitting and Inferential issues, Evaluation, Use of Data Mining packages with applications for benchmark and real world situations.


Subject Learning Outcomes
On successful completion of this subject, students will be able to:
1. Identify useful relationships and important subgroups in large data sets.
2. Suggest appropriate approaches and solutions to given data mining problems.
3. Plan and carry out analyses of large and complex data sets.
4. Use parametric, non-parametric, and probabilistic methods to model data in various domains.
5. Analyse and interpret results
6. Use data mining software such as R as well as use relevant plugins and software packages.
7. Analyse data mining algorithms and techniques.
8. Understand the role and challenges of methods in Big Data applications.
9. Identify and distinguish data mining applications from other IT applications.
10. Describe data mining algorithms.
11. Compare the applicability of data mining applications.

Extra Information
Generic Extra Information:
Knowledge of mathematical and statistical notation at an introductory level is assumed.

Textbook Information

Text book information is available via the UniShop website:



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