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 [HUANG97] [HUANG98]
• k-modes with initialization based on density [CAO09]
• k-prototypes [HUANG97]

The code is modeled after the clustering algorithms in scikit-learn and has the same familiar interface.

I would love to have more people play around with this and give me feedback on my implementation. If you come across any issues in running or installing kmodes, please submit a bug report.

Enjoy!

kmodes can be installed using pip:

pip install kmodes

To upgrade to the latest version (recommended), run it like this:

pip install --upgrade kmodes

Alternatively, you can build the latest development version from source:

git clone https://github.com/nicodv/kmodes.git
cd kmodes
python setup.py install

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)

clusters = km.fit_predict(data)

# Print the cluster centroids
print(km.cluster_centroids_)

The examples directory showcases simple use cases of both k-modes ('soybean.py') and k-prototypes ('stocks.py').

The k-modes algorithm accepts np.NaN values as missing values in the X matrix. However, users are strongly suggested to consider filling in the missing data themselves in a way that makes sense for the problem at hand. This is especially important in case of many missing values.

The k-modes algorithm currently handles missing data as follows. When fitting the model, np.NaN values are encoded into their own category (let's call it "unknown values"). When predicting, the model treats any values in X that (1) it has not seen before during training, or (2) are missing, as being a member of the "unknown values" category. Simply put, the algorithm treats any missing / unseen data as matching with each other but mismatching with non-missing / seen data when determining similarity between points.

The k-prototypes also accepts np.NaN values as missing values for the categorical variables, but does not accept missing values for the numerical values. It is up to the user to come up with a way of handling these missing data that is appropriate for the problem at hand.