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03/26/11 19:44:38 (
Possible mentors: Matija
== Matrix factorization techniques for data mining ==
Matrix factorization is a fundamental building block for many of current data mining approaches. Factorization techniques, like non-negative and probabilistic sparse matrix factorizations are today widely used in various applications of data mining. The aim of this project is to develop a scripting library for Orange that include various matrix factorization techniques, document the code, provide examples that demonstrate various types of applications, include the examples in the documentation. The entire development would be for scripting, that is, the project would not involve any widget programming. We would, though, like to have the student sketch how should the widgets that use this library look like, which methods developed in the scripting library should they access, and which (if any) are useful visualizations to be implemented.
Useful skills: Python. Matrix operations (possibly in numpy). Good background in math, linear algebra and optimization.
Level from 1 (beginner) to 5 (professional): 4
Possible mentors: Blaz
== Test scripts, example scripts and documentation ==