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.

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.