K-Means Clustering Algorithm For Pair Selection In Python – Part VIII


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See the prior installments in this series: Part I, Part II, Part III, Part IVPart V , Part VI and Part VII.

Now that we have imported our data analysis, visualization and the make_blobs method from sklearn, we’re ready to create our toy data to begin our analysis.

#creating fake data
data=make_blobs(n_samples=500, n_features=8,centers=5, cluster_std=1.5, random_state=201)

In the above line of code, we have created a variable named data and have initialized it using our make_blobs object imported from sklearn. The make blobs object allows us to create and specify the parameters associated with the data we’re going to create. We’re able to assign the number of samples, or the number of observations equally divided between clusters, the number of features, clusters, cluster standard deviation, and a random state. Using the centres variable, we can determine the number of clusters that we want to create from our toy data.

Now that we have initialized our method, let’s take a look at our data.

#Let’s take a look at our fake data
data[0] #produces an array of our samples

K-Means Clustering Algorithm For Pair Selection In Python

Stay tuned -for the next installment in this series. Lamarcus will view the cluster assignments we created.

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