prob-clustering

Unsupervised clustering algorithm based on Lloyd’s algorithm. Key points: • works only with binary vectors, • example-centroid distances are estimated as the norm of differences between a feature and its likelihood, p(x|y) • performance is assessed as the mean entropy across all examples

To-do list:

1. Optimise code to exclude all for loops,
2. find a better way to deal with datasets with linearly dependent columns. At present it will simply compute the determinant of the co-variance matrix between all features and issue a warning if it's non-positive.

UPDATE:6.5.2017: added further code incorporating Mahalanobis distance