k-modes/k-prototypes聚類算法Python實現：kmodes
k-modes/k-prototypes聚類算法Python實現。
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 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!
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)