kmodes 0.4

Implementation of the k-modes and k-prototypes clustering algorithms

kmodes

Description

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.

Simple usage examples of both k-modes (‘soybean.py’) and k-prototypes (‘stocks.py’) are included in the examples directory.

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!

Installation

kmodes can be installed using pip:

pip install 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

Usage

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)

References

[HUANG97] (1,2)

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.

[HUANG98]

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.

[CAO09]

Cao, F., Liang, J, Bai, L.: A new initialization method for categorical data clustering, Expert Systems with Applications 36(7), pp. 10223-10228., 2009.