Title: Mini-Batch k-Means Clustering
Slug: minibatch_k-means_clustering
Summary: How to conduct mini-batch k-means clustering in scikit-learn.
Date: 2017-09-22 12:00
Category: Machine Learning
Tags: Clustering
Authors: Chris Albon

Mini-batch k-means works similarly to the k-means algorithm discussed in the last recipe. Without going into too much detail, the difference is that in mini-batch k-means the most computationally costly step is conducted on only a random sample of observations as opposed to all observations. This approach can significantly reduce the time required for the algorithm to find convergence (i.e. fit the data) with only a small cost in quality.

Preliminaries



In [1]:

    
# Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import MiniBatchKMeans

Load Iris Flower Dataset



In [2]:

    
# Load data
iris = datasets.load_iris()
X = iris.data

Standardize Features



In [3]:

    
# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

Conduct k-Means Clustering

MiniBatchKMeans works similarly to KMeans, with one significance difference: the batch_size parameter. batch_size controls the number of randomly selected observations in each batch. The larger the the size of the batch, the more computationally costly the training process.



In [4]:

    
# Create k-mean object
clustering = MiniBatchKMeans(n_clusters=3, random_state=0, batch_size=100)

# Train model
model = clustering.fit(X_std)