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an implementation of spectral clustering for text document collections

Project description



Spectral clustering a modern clustering technique considered to be effective for image clustering among others. [1] [2]

This software find clusters among documents based on the bag-of-words representation [3] and TF-IDF weighting [4].

[1]Ulrike von Luxburg, A Tutorial on Spectral Clustering, 2006.
[2]Chris H. Q. Ding, Spectral Clustering, 2004.


Following softwares are required.

  • Python 2 or 3
  • Numpy
  • Scipy

How to use

  1. Prepare documents as raw-text files, and put them in a directory, for example, ‘reuters’.

  2. Prepare a category file. For example, ‘cats.txt’ may contain:

    14833 palm-oil veg-oil
    14839 ship

    This means that the file ‘14833’ has ‘palm-oil’ and ‘veg-oil’ as its categories, and ‘14839’ has ‘ship’ as its category.

  3. Run: python scluster/ cats.txt reusters/ -m kmeans,


  • When you use the Reuters set, notice No 17980 might contain non-Unicode character at Line 10. It should probably read: “world economic growth-side measures …”

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