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What is Clustering?

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Beginning here with an example:

“Joe Biden wins US election” – Washington Post

“A massive win for Joe Biden in the closest election ever ran in the US history” – New York Times

“Joe Biden wins the presidential race” – Wall Street Journal.

All the above news items are similar in nature from a newsreader’s perspective, and therefore it makes no sense for the person to read three news. News media sites such as Google News use Clustering methods to bundle similar news articles under one topic while segregating other news.

More formally, Clustering is a technique in unsupervised machine learning that partitions a set of data points into groups, called clusters, based on their similarities. The goal of clustering is to separate data points into meaningful groups, such that the data points within each group are as similar as possible to each other, while being as dissimilar as possible to data points in other groups. Clustering algorithms are widely used in various applications, such as customer segmentation, image segmentation, and anomaly detection.

There are several types of clustering algorithms, including k-means clustering, hierarchical clustering, and density-based clustering. The choice of the appropriate clustering algorithm depends on the characteristics of the data set and the desired outcome.

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