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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:
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!
Installation
kmodes can be installed using pip:
pip install kmodesAlternatively, you can build the latest development version from source:
git clone https://github.com/nicodv/kmodes.git
cd kmodes
python setup.py installUsage
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)
# Print the cluster centroids
print(km.cluster_centroids_)
clusters = km.fit_predict(data)More simple usage examples of both k-modes (‘soybean.py’) and k-prototypes (‘stocks.py’) are included in the examples directory.
Missing / unseen data
The k-modes algorithm accepts np.NaN values as missing values in
the X matrix. When fitting the model, these 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.
References
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.
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.
Cao, F., Liang, J, Bai, L.: A new initialization method for categorical data clustering, Expert Systems with Applications 36(7), pp. 10223-10228., 2009.
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