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To locate groupings that haven't been explicitly identified in the data, the K-means clustering technique is utilised. This can be used to verify business assumptions about the types of groups that exist, as well as to identify unknown groups in large data sets.

Advantages of K Means Clustering:

  • It can be used to identify patterns in data without any prior knowledge about the data.
  • It can be used to find natural groups or clusters in data even if they are not completely separated from each other.
  • It can be used to find natural groups or clusters in data with different shapes, sizes, and densities.

The algorithm starts by randomly selecting a point, called a cluster center, and then assigns each point in the dataset to one of these clusters. The algorithm then iterates over all points again until it converges on the desired number of clusters.

K means are used to divide data points into discrete, non-overlapping groupings. One of the most common uses of K means clustering is client segmentation in order to gain a better understanding of them, which can then be used to boost the company's income.

Customer segmentation divides a market into many different groups of customers with comparable qualities. Market segmentation is a powerful tool for defining and meeting client needs.

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