Data mining is the computational process of discovering patterns from large data sets. This unit discusses concepts and techniques of data warehousing and mining. Data mining is one of the most advanced tools used by IT industries. The topics covered include data warehouse models, data pre-processing, Online Analytical Processing, association rules mining, classification, clustering, sequential data mining and neural networks for data mining. In addition, students will learn how to use and apply relevant commercial data mining software to find solutions to real life business problems. This unit complements the student knowledge of database systems.

Unit details

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Credit points:
Unit code:


NIT5130 - Database Analysis and Design

Learning Outcomes

On successful completion of this unit, students will be able to:
  1. Critically analyse the features and applications of data warehouses;  
  2. Disaggregate and appraise the components in a typical data warehouse architecture;  
  3. Extrapolate knowledge and skills to design a data warehouse to support and provide business solutions;  
  4. Investigate and apply knowledge discovery processes and associated algorithms to large business datasets; and  
  5. Experiment with popular data mining software and propose a conceptual framework to evaluate its useability and functionality.  


Assessment type Description Grade
Assignment Assignment 1 - Development of data warehouse (1000 word report and 200 line codes) 20%
Assignment Assignment 2 - Data mining project, soft code and analysis report (1000 word report and 200 line codes) 20%
Examination Final Exam (3 hours) 60%

Required reading

Data Mining: Concepts and Techniques 3rd ed.
Han, J., and Kamber, M. (2011)
Morgan Kaufmann

Where to next?

As part of a course

This unit is studied as part of the following courses. Refer to the course page for information on how to apply for the course.

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