customer segmentation using k means

How clustering is used in market segmentation?
Cluster analysis groups data points together so that they are similar to one another.
This information can then be used to identify groups of consumers with similar needs or purchasing behaviors.
Marketers can then target their marketing efforts toward these specific groups.
What is K-Means segmentation algorithm?
K-Means algorithm uses the clustering method to group identical data points in one group and all the data points in that group share common features but are distinct when compared to data points in other groups.
Points in the same group are similar as possible.
Points in different groups are as dissimilar as possible.
What is KNN for customer segmentation?
KNN, or k-nearest neighbors, is a popular machine learning algorithm for segmentation, or grouping similar data points together.
It works by finding the closest neighbors of a new data point based on a distance metric, and assigning it to the most common class among them.
The k-means algorithm, a popular method for clustering data points based on their similarity, is employed in this study to segment the mall customers.
K-means clustering is a form of unsupervised machine learning and is useful in identifying patterns and natural groupings within a dataset.

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Customer Segmentation using K-means Clustering - IEEE Xplore

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