How to apply Kmeans using Dask?

How to apply Kmeans using Dask?

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.

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)

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