COMPX523-20A (HAM)

Data Stream Mining

15 Points

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Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences
Department of Computer Science

Staff

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Convenor(s)

Albert Bifet Figuerol

Albert Bifet Figuerol
See Moodle
albert.bifetfiguerol@waikato.ac.nz

Heitor Gomes

Heitor Gomes
FG.2.03
See Moodle
heitor.gomes@waikato.ac.nz

Lecturer(s)

Albert Bifet Figuerol

Albert Bifet Figuerol
See Moodle
albert.bifetfiguerol@waikato.ac.nz

Jacob Montiel Lopez

Jacob Montiel Lopez
9641
FG.2.04
See Moodle
jacob.montiellopez@waikato.ac.nz

Administrator(s)

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: debby.dada@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
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Paper Description

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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 (Java) and scikit-multiflow (Python), two open source stream mining softwares currently being developed by the Machine Learning group.
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Paper Structure

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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.
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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Students will be able to select and apply appropriate algorithms for data stream mining problems.
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  • Students will be able to design and implement new algorithms in a data stream mining framework like MOA, or similar.
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  • Students will be able to compare and evaluate different algorithms/solutions for a problem and summarize their findings in a report.
    Linked to the following assessments:
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Assessment

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Assignment 1
15
  • Online: Submit through Moodle
2. Assignment 2
35
  • Online: Submit through Moodle
3. Test 1
30 Apr 2020
No set time
25
4. Test 2
4 Jun 2020
No set time
25
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Required Readings

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https://mitpress.mit.edu/books/machine-learning-data-streams

There is an official online version available at https://moa.cms.waikato.ac.nz/book/

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Recommended Readings

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Knowledge Discovery from Data Streams, by Joao Gama
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Online Support

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Moodle is used for this paper.
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Workload

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About 150 to 180 hours in total, including lecture, reading time, assignment, and presentation and report preparation.
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Linkages to Other Papers

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Three 300 level Computer Science papers, including COMP321 Practical Data Mining or COMP316 Artificial Intelligence Techniques and Applications Corresponding Papers COMP523 Data Stream Mining
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Prerequisite(s)

Prerequisite papers: COMPX305 or COMPX310 or COMP316 or COMP321 and a further 30 points at 300 level in Computer Science

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: COMP423, COMP523

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