UWA Handbook 2017

Unit details

CITS5508 Advanced Data Mining

Credit 6 points
  Offering
(see Timetable)
AvailabilityLocationMode
Not available in 2017UWA (Perth)Face to face
Content There is an explosion in data generation and data collection due to improvements in sensing technologies and business processes. Extracting meaningful knowledge from large amounts of data has become a priority for businesses as well as scientific domains. Data mining tools are essential for knowledge extraction from large volumes of data. In this unit, students develop in-depth understanding of data mining techniques that will be applicable both for scientific and business data. The topics include association rule mining or, market basket analysis; unsupervised machine learning techniques like k-means clustering, density based clustering, hierarchical clustering and subspace clustering; supervised machine learning techniques including artificial neural networks, decision trees, support vector machines and Bayesian belief networks.
Outcomes Students are able to (1) understand the role of data mining in knowledge extraction; (2) identify appropriate data mining techniques for a given problem; (3) understand the difference between supervised and unsupervised learning algorithms; (4) understand the specific details of individual data mining algorithms; (5) implement a data mining solution for a real-world dataset; and (6) develop the ability to analyse large datasets from the perspective of data mining.
Assessment Typically this unit is assessed in the following ways: (1) test; (2) group project; and (3) a final examination. Further information is available in the unit outline.

Supplementary assessment is only available in this unit in the case of a student who has obtained a mark of 45 to 49 and is currently enrolled in this unit, and it is the only remaining unit that the student must pass in order to complete their course.
Unit Coordinator(s) Professor Associate Professor Ajmal Mian
Unit rules
Prerequisites: enrolment in the Master of Data Science or the Master of Information Technology
Contact hours lectures: 2 hours per week; labs: 2 hours per week for 11 weeks from week 2
Unit Outlinehttp://www.unitoutlines.ecm.uwa.edu.au/Units/CITS5508/SEM-1/2017

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