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CP5634 - Data Mining

Credit points: 3
Year: 2019
Student Contribution Band: Band 2
Administered by: College of Science and Engineering

This subject focuses on advanced data mining techniques for intelligence informatics. It provides an in-depth coverage of the need for big data analysis, data warehousing, big data analytics, predictive methods, scalability considerations, data visualisation, and data mining techniques. Students will gain hands-on experience with various data mining tools and embedding data mining in intelligent informatics solutions.

Learning Outcomes

  • explain the importance of big data analysis and data mining;
  • identify and critically evaluate data mining techniques and tools;
  • compare and evaluate appropriate techniques for clustering, classification and association rules mining;
  • assess the potential benefits, risks, issues and challenges associated with big data and data mining;
  • explore and analyse data mining patterns for intelligence informatics.
Inadmissible
Subject
Combinations:
CP3300 CP3403 CP5605

Availabilities

Townsville, Internal, Study Period 1
Census Date 28-Mar-2019
Coord/Lect: Professor Ickjai Lee.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours on-campus > Lectures
  • 12 hours on-campus > Practicals
  • 6 hours on-campus > Workshops/Seminars
  • assessment and self-directed study
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Singapore, Internal, Study Period 51
Census Date 11-Apr-2019
Coordinator: Professor Ickjai Lee
Lecturer: Dr Kisenchand Ranai.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 44 hours - Combined Lectures; Tutorials/Workshops/ Practicals; Lecturer directed activities
  • assessment and self-directed study
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Singapore, Internal, Study Period 52
Census Date 08-Aug-2019
Coordinator: Professor Ickjai Lee
Lecturer: Mr Kwang Lim.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 44 hours - Combined Lectures; Tutorials/Workshops/ Practicals; Lecturer directed activities
  • assessment and self-directed study
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Brisbane, Internal, Study Period 21
Census Date 11-Apr-2019
Coordinator: Professor Ickjai Lee
Lecturer: Dr Paul Darwen.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours on-campus > Lectures
  • 12 hours on-campus > Practicals
  • 6 hours on-campus > Workshops/Seminars
  • assessment and self-directed study
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Brisbane, Internal, Study Period 22
Census Date 08-Aug-2019
Coordinator: Professor Ickjai Lee
Lecturer: Dr Paul Darwen.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours on-campus > Lectures
  • 12 hours on-campus > Practicals
  • 6 hours on-campus > Workshops/Seminars
  • assessment and self-directed study
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Brisbane, Internal, Study Period 23
Census Date 05-Dec-2019
Coordinator: Professor Ickjai Lee
Lecturer: Dr Paul Darwen.
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours on-campus > Lectures
  • 12 hours on-campus > Practicals
  • 6 hours on-campus > Workshops/Seminars
  • assessment and self-directed study
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.