Data Stream Mining - COMPX523 (2020)

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): COMPX305 or COMPX310 or COMP316 or COMP321 and a further 30 points at 300 level in Computer Science
Internal assessment / examination: 100:0
Restriction(s): COMP423, COMP523

Semesters and Locations

Occurrence Code When taught Where taught
20A (HAM)A Semester : 2 Mar 2020 - 28 Jun 2020 Hamilton

Timetabled Lectures for Data Stream Mining (COMPX523)

DayStartEndRoomDates
Mon9:00 AM10:00 AMG.1.15Mar 2 - May 31
Thu10:00 AM11:00 AMG.1.15Mar 2 - Jun 7

NB:There may be other timetabled events for this paper such as tutorials or workshops.
Visit the online timetable for COMPX523 for more details


Indicative Fees for Data Stream Mining (COMPX523)

Occurrence Domestic International
 Tuition Resource 
20A (HAM) $1052 $0 $4293
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

The following 2019 paper outlines are available for COMPX523. Please contact the Faculty or School office for details on 2020 outlines.

Additional Information

Available Subjects:  Computer Science | Electrical and Electronic Engineering | Software Engineering

Other available years: Data Stream Mining - COMPX523 (2019)

Paper details current as of : 20 November 2019 2:21pm
Indicative fees current as of : 12 November 2019 11:10am

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