How to do spectral clustering using Dask?
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# How to do spectral clustering using Dask?

This recipe helps you do spectral clustering using Dask

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## Recipe Objective

How to do spectral clustering using Dask.

Spectral clustering scales the number of samples as per the model requirement, The Dask version uses an approximation to the affinity matrix, which reduces expensive computation.

``` #!pip install dask_ml --upgrade #!pip install dask distributed --upgrade ```

## Step 1- Importing Libraries

We will import the dataset make_circles and the clusters from dask_ml.

``` from sklearn.datasets import make_circles from sklearn.utils import shuffle import pandas as pd from timeit import default_timer as tic import dask_ml.cluster ```

## Step 2- Spllting dataset.

We will split the dataset into x and y to feed into clustering algorithm.

`x, y = make_circles(n_samples=10_000, noise=0.05, random_state=0, factor=0.2)`

## Step 3- Creating Clusters.

We will do the spectral clustering, with defining the clusters and n_components, then we will fit the model.

``` Ns = [500, 1000, 2500, 5000] timings = [] for n in Ns: t1 = tic() dask_ml.cluster.SpectralClustering(n_clusters=2, n_components=100).fit(x) timings.append(('dask-ml (approximate)', n, tic() - t1)) df = pd.DataFrame(timings, columns=['method', 'Samples', 'Fitting Time']) df ```

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