Data Stream Mining - COMP423 (2017)

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: 1:0
Restriction(s): COMP523

COMP423-17B (HAM) has been cancelled. Please contact the School or Faculty Office for more information.

Semesters and Locations

Occurrence Code When taught Where taught
17A (HAM)A Semester : Feb 27 - Jun 25, 2017 Hamilton
17B (HAM)B Semester : Jul 10 - Oct 29, 2017 Hamilton

Timetabled Lectures for Data Stream Mining (COMP423)

DayStartEndRoomDates
Mon10:00 AM11:00 AMG.1.15Feb 27 - Jun 4
Thu10:00 AM11:00 AMG.1.15Feb 27 - Jun 4

NB:There may be other timetabled events for this paper such as tutorials or workshops.
Visit the online timetable for COMP423 for more details


Indicative Fees for Data Stream Mining (COMP423)

Occurrence Domestic International
 Tuition Resource 
17A (HAM) $848 $0 $3497
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 (COMP423)

The following paper outlines are available for Data Stream Mining (COMP423).
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 - COMP423 (2018)

Paper details current as of : 19 January 2018 11:30am
Indicative fees current as of : 20 January 2018 4:30am

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