2015 Papers
Level 4 Papers >> All Papers
COMP423B Data Stream Mining

15 Points

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. This paper focuses on, as well as extends the methods implemented in MOA, an open source stream mining software suite currently being developed by the Machine Learning group.

Learning Outcomes
Students will be able to: select and apply appropriate algorithms for data stream mining problems; design and implement new algorithms in a data stream mining framework like MOA or similar; compare and evaluate different algorithms/solutions for a problem and summarize in a report.

Pre Requisite Papers
Three 300 level Computer Science papers, including
COMP321 Practical Data Mining or
COMP316 Artificial Intelligence Techniques and Applications

Corresponding Papers
COMP523 Data Stream Mining

Lecturers and Tutors

Timetable
Official Timetable Information

Compulsory Components

Workload
About 10 hours a week on average.

Required Reading
Knowledge Discovery from Data Streams, by Joao Gama

Assessment Ratio
Internal assessment/final examination ratio 1:0

Grading Schedule
see Moodle page

Assessment Deadlines
see Moodle page

Attendance Policy
Class attendance is expected. The course notes provided are not comprehensive, additional material will be covered in class. You are responsible for all material covered in class.

Academic Integrity
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Performance Impairment
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Student Concerns and Complaints
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Application for Extension
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Review of Grade
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University Regulations
Your attention is drawn to the following regulations and policies, which are published in the University Calendar:

  2007 FCMS. The University of Waikato - Te Whare Wananga o Waikato