Kmodes

- Improved pandas compatibility, courtesy of Genie-Liu
- Forward compatible with future scikit-learn versions that will no longer include joblib, courtesy of trevorstephens

- Support for more than 256 clusters
- Optional parallel execution of the multiple initialization runs (courtesy of rphes )
- Enhanced error checking when using pandas DataFrames as inputs to the algorithms
- Various bug fixes and improvements
- Semantic versioning from now on

- Bugfixes

- Huge speedup for k-prototypes, especially for large numbers of samples (45). A k-prototypes benchmark script is included in examples now.
- Offer an implementation of Ng's dissimilarity measure, which could improve convergence (37).
- Allow pandas DataFrames to be presented to the algorithm, instead of just numpy arrays (40).
- Improved handling of dependencies (49, 53).
- Various small bugfixes and improvements.

- Categorical variables are now automatically encoded and decoded between original data values and integers (used internally by k-modes). User does not have to use to the categorical variable mapping anymore when looking at the cluster centroids.
- Support for custom dissimilarity measures
- Python 3.6 support
- More robust manual initialization