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  • K_Means_Clustering_
  • 6.2 K-Means Clustering

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FAQs

K-means clustering assigns every data point to the nearest centroid, which are K separate randomly-initiated points in the data. The centroid is shifted to the average of all the points assigned to it once each point has been assigned.

K-means is a data clustering approach that can be applied to unsupervised machine learning. It can sort unlabeled data into a preset number of clusters based on their similarities (k).

Clustering is an unsupervised machine learning technique for discovering and grouping related data points in huge datasets without regard for the outcome. Clustering (also known as cluster analysis) is a technique for organising data into structures that are easier to comprehend and manipulate.

The number of clusters that is ideal can be calculated as follows: Calculate different values of k using a clustering technique (e.g., k-means clustering). Changing k from 1 to 10 clusters, for example. Calculate the total within-cluster sum of squares for each k. (wss).

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