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CP5634 - Data Mining

Credit points: 3
Year: 2023
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.

Subject Assessment

  • Written > Examination (centrally administered) - (40%) - Individual
  • Written > Test/Quiz 1 - (20%) - Individual
  • Written > Project report - (40%) - Group.
Inadmissible
Subject
Combinations:
CP3300 CP3403 CP5605

Availabilities

Cairns, Study Period 1, Internal
Census Date 23-Mar-2023
Coord/Lect: Dr Dmitry Konovalov.
Workload expectations:

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

  • 26 hours lectures
  • 6 hours workshops
  • 12 hours specialised
  • assessment and self-directed study

JCU Brisbane, Trimester 1, Internal
Census Date 09-Mar-2023
Coordinator: Dr Dmitry Konovalov
Lecturer: Dr Ahmad Abdel-Hafez.
Workload expectations:

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

  • 26 hours lectures
  • 6 hours workshops
  • 12 hours specialised
  • assessment and self-directed study

JCU Brisbane, Trimester 2, Internal
Census Date 22-Jun-2023
Coordinator: Dr Dmitry Konovalov
Lecturer: Dr Paul Darwen.
Workload expectations:

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

  • 26 hours lectures
  • 6 hours workshops
  • 12 hours specialised
  • assessment and self-directed study

JCU Brisbane, Trimester 3, Internal
Census Date 05-Oct-2023
Coordinator: Dr Dmitry Konovalov
Lecturer: Dr Paul Darwen.
Workload expectations:

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

  • 26 hours lectures
  • 6 hours workshops
  • 12 hours specialised
  • assessment and self-directed study

JCU Singapore, Study Period 51, Internal
Census Date 06-Apr-2023
Coordinator: Dr Dmitry Konovalov
Lecturer: Dr Eric Tham.
Workload expectations:

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

  • 26 hours lectures
  • 6 hours workshops
  • 12 hours specialised
  • assessment and self-directed study

JCU Singapore, Study Period 52, Internal
Census Date 03-Aug-2023
Workload expectations:

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

  • 26 hours lectures
  • 6 hours workshops
  • 12 hours specialised
  • assessment and self-directed study

Townsville, Study Period 1, Internal
Census Date 23-Mar-2023
Coord/Lect: Dr Dmitry Konovalov.
Workload expectations:

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

  • 26 hours lectures
  • 6 hours workshops
  • 12 hours specialised
  • assessment and self-directed study

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.