How to apply Kmeans using Dask?
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How to apply Kmeans using Dask?

How to apply Kmeans using Dask?

This recipe helps you apply Kmeans using Dask

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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) kmeans.fit(X)

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