Level 1 Papers

Level 2 Papers

Level 3 Papers

Level 4 Papers

Level 5 Papers

Summer School Papers

Industry Papers

Topics in Operating Systems

Topics in Computer Networks

Machine Learning Algorithms

Data Stream Mining

Usability Engineering

Web Search: Technical and Social Issues

Developing Mobile Applications

Location-Based Systems as Context-Aware Systems

Model Checking

Extremely Parallel Programming

Turing Topics

Report of an Investigation

2014 Papers
Level 4 Papers >> A Semester
COMP423A 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

A Semester

Moodle Link
Moodle COMP423/523

Official Timetable Information

Compulsory Components
COMP423 students can skip one of the items of assessment as specified for COMP523. If they attempt all items, their worst one will be removed. The final total will be re-scaled to ensure that the optimal score achievable is 100%.

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
Follow this link for Academic Integrity information and this link for detailed explanation of How to prevent plagiarism in Computer Science assessment items.

Performance Impairment
Follow this link for information on Performance Impairment.

Student Concerns and Complaints
Follow this link for Student Concerns and Complaints information.

Application for Extension
Follow this link for information on applying for an Extension.

Review of Grade
Follow this link for information on applying for a Review of Grade.

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