COMP523

Data Stream Mining

Data streams are everywhere, from F1 racing over electricity networks to news feeds. Data stream mining relies on and develops new incremental algorithms that process streams under strict resource limitations.

Data streams are everywhere, from F1 racing over electricity networks to news feeds. Data stream mining relies on and develops new incremental algorithms that process streams under strict resource limitations.

Paper Information

Points: 15.0
Prerequisite(s): Either COMP316 or COMP321, and a further 40 points at 300 level in Computer Science.
Internal assessment / examination: 100:0
Restriction(s): COMP423

Semesters and Locations

Occurrence Code When taught Where taught
18A (HAM)A Semester : Feb 26 - Jun 24, 2018 Hamilton

Timetabled Lectures for Data Stream Mining (COMP523)

DayStartEndRoomDates
Mon9:00 AM10:00 AMG.1.15Feb 26 - Jun 3
Thu10:00 AM11:00 AMG.1.15Feb 26 - Jun 3

NB:There may be other timetabled events for this paper such as tutorials or workshops.
Visit the online timetable for COMP523 for more details


Indicative Fees for Data Stream Mining (COMP523)

Occurrence Domestic International
 Tuition Resource 
18A (HAM) $1011 $0 $4008
You will be sent an enrolment agreement which will confirm your fees.
Tuition fees shown below are indicative only and may change. There are additional fees and charges related to enrolment - please see the Table of Fees and Charges for more information.

Paper Outlines for Data Stream Mining (COMP523)

The following paper outlines are available for Data Stream Mining (COMP523).
If your paper occurrence is not listed contact the Faculty or School office.

Additional Information

Available Subjects:  Computer Science

Other available years: Data Stream Mining - COMP523 (2017)

Paper details current as of : 21 May 2018 4:26pm
Indicative fees current as of : 24 May 2018 4:30am

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