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CSC8004 Data Mining

Semester 1, 2022 Online
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Mathematics, Physics & Computing
Student contribution band : Band 2
Grading basis : Graded
Version produced : 30 June 2022

Staffing

Examiner: Ji Zhang

Requisites

Pre-requisite: (STA2300 or STA1003 or STA8170) 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:

  1. Demonstrate advanced and integrated understanding of the basic data mining tasks and concepts
  2. Analyse critically and evaluate data mining problems
  3. Apply knowledge and skills to key algorithms in data mining applications
  4. Apply knowledge, expert judgement and problem-solving skills to real world data mining problems
  5. 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

Jiawei, H 2012, Data Mining: Concepts and Techniques, 3rd edn, Morgan Kaufmann.

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

Approach Type Description Group
Assessment
Weighting (%) Course learning outcomes
Assignments Written Problem Solving 1 No 10 1
Assignments Written Problem Solving 2 No 20 1,3
Assignments Written Report No 70 2,3,4,5
Date printed 30 June 2022