| Semester 1, 2023 Online | |
| Units : | 1 |
| School or Department : | School of Mathematics, Physics & Computing |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Course Coordinator:
Requisites
Pre-requisite or Co-requisite: (STA2300 or STA1003 or STA8170 or STA6200) and (CSC1401 or CSC5020)
Overview
Data mining is an interdisciplinary field which brings together techniques of machine learning, database, information retrieval, mathematics and statistics. These techniques are used to find useful patterns in large datasets. Methods for such knowledge discovery in data bases are required owing to the size and complexity of data collection in administration, business and science.
Data mining aims at finding useful regularities or patterns in large data sets generated in modern management and science. This course covers the main data mining methods, including clustering, classification, association rules mining, and recent techniques for data mining. The methods are developed and applied to various data sets.
Course learning outcomes
On successful completion of this course students should be able to:
- Demonstrate advanced and integrated understanding of the basic data mining tasks and concepts
- Analyse critically and evaluate data mining problems
- Apply knowledge and skills to key algorithms in data mining applications
- Apply knowledge, expert judgement and problem-solving skills to real world data mining problems
- Analyse critically and reflect on the effectiveness and estimate the performance of data mining algorithms
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Data pre-processing and preparation | 10.00 |
| 2. | Associate rule mining | 20.00 |
| 3. | Descriptive data mining (Clustering) | 20.00 |
| 4. | Predictive data mining (Classification) | 30.00 |
| 5. | Outlier Detection | 20.00 |
Text and materials required to be purchased or accessed
(the 3rd edition of the textbook is also acceptable.)
Student workload expectations
To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.
Assessment details
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Problem Solving 1 | No | 10 | 1 |
| Problem Solving 2 | No | 20 | 1,3 |
| Report | No | 70 | 2,3,4,5 |