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Possible Mentors

  • Jamie Bullock


Clustering is the assignment of objects into groups (called clusters) so that objects from the same cluster are more similar to each other than objects from different clusters. Often similarity is assessed according to a distance measure. Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. The purpose of this project is to develop a set of tools in the form of Pd patches and abstractions that make various clustering techniques available in Pd. This could include (but not limited to) k-Means clustering, Principal Components Analysis and Multi-dimensional Scaling analysis.

Related projects

Resources to start:

Required Skills

  • knowledge and interest in data clustering approaches

  • decent mathematical skills (geometry)

  • reasonable Pd patching skills

  • possible C, Python or Lua skills

Possible Breakdown of Steps

  • Investigate existing collections of abstractions/patches that fit the requirements

  • Identify the set of abstractions/patches that will form the basis of the library

  • Create new abstractions/patches as necessary

  • Package up the abstractions for distribution

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