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.
|Prerequisite(s):||Prerequisite papers: Either COMP316 or COMP321, and a further 40 points at 300 level in Computer Science.|
|Internal assessment / examination:||100:0|
Semesters and Locations
|Occurrence Code||When taught||Where taught|
|18A (HAM)||A Semester : 26 Feb 2018 - 24 Jun 2018||Hamilton|
Timetabled Lectures for Data Stream Mining (COMP523)
|Mon||9:00 AM||10:00 AM||G.1.15||Feb 26 - Jun 3|
|Thu||10:00 AM||11:00 AM||G.1.15||Feb 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)
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.
Available Subjects: Computer Science
Other available years: Data Stream Mining - COMP523 (2017)
Paper details current as of : 19 July 2018 11:44am
Indicative fees current as of : 5 June 2018 4:30am