Python implementations of the k-modes and k-prototypes clustering algorithms. Relies on numpy for a lot of the heavy lifting.
k-modes is used for clustering categorical variables. It defines clusters based on the number of matching categories between data points. (This is in contrast to the more well-known k-means algorithm, which clusters numerical data based on Euclidean distance.) The k-prototypes algorithm combines k-modes and k-means and is able to cluster mixed numerical / categorical data.
Implemented are:
- k-modes [1][2]
- k-modes with initialization based on density [3]
- k-prototypes [1]
The code is modeled after the k-means module in scikit-learn and has the same familiar interface.
Usage examples of both k-modes ('soybean.py') and k-prototypes ('stocks.py') are included.
I would love to have more people play around with this and give me feedback on my implementation.
Enjoy!
```python import numpy as np from kmodes import kmodes
# random categorical data data = np.random.choice(20, (100, 10))
km = kmodes.KModes(n_clusters=4, init='Huang', n_init=5, verbose=1) km.fit_predict(data) ```
[1] Huang, Z.: Clustering large data sets with mixed numeric and categorical values, Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference, Singapore, pp. 21-34, 1997.
[2] Huang, Z.: Extensions to the k-modes algorithm for clustering large data sets with categorical values, Data Mining and Knowledge Discovery 2(3), pp. 283-304, 1998.
[3] Cao, F., Liang, J, Bai, L.: A new initialization method for categorical data clustering, Expert Systems with Applications 36(7), pp. 10223-10228., 2009.