How to apply Kmeans using Dask?
This recipe helps you apply Kmeans using Dask
Recipe Objective
How to apply Kmeans using Dask
Most of the estimators in Sci-kit Learn are programmed to work on in-memory arrays. To train the larger datasets we require different algorithms.
In Dask we use K-means clustering technique to cluster the large data.
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Table of Contents
Step 1- Importing Libraries.
#! pip install dask_ml
import dask_ml.datasets
import dask_ml.cluster
Step 2- Splitting the datasets.
Arranging the datasets into X,y to process.
dask_ml.datasets
X, y = dask_ml.datasets.make_blobs(n_samples=100000000,chunks=10000,random_state=0,centers=4)
X = X.persist()
X
Step 3- Creating clusters.
Creating clusters by applying kmeans, dividing dataset into 4 clusters.
kmeans = dask_ml.cluster.KMeans(n_clusters=4, init_max_iter=1, oversampling_factor=8)
kmeans.fit(X)
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