Introduction To Elbow Method

Introduction To Elbow Method

A fundamental step for any unsupervised algorithm is to determine the optimal number of clusters into which the data may be clustered. Since we do not have any predefined number of clusters in unsupervised learning. We tend to use some method that can help us decide the best number of clusters.  In the case of K-Means clustering, we use Elbow Method for defining the best number of clustering...

What Is the Elbow Method in K-Means Clustering

As we know in the k-means clustering algorithm we randomly initialize k clusters and we iteratively adjust these k clusters till these k-centroids riches in an equilibrium state. However, the main thing we do before initializing these clusters is that determine how many clusters we have to use....

Implementation of the Elbow Method Usking Sklearn in Python

We will see how to implement the elbow method in 4 steps. At first, we will create random dataset points, then we will apply k-means on this dataset and calculate wcss value for k between 1 to 4....

Clustered Data Points For Different k Values

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