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Data mining and clustering are closely interlinked. They both focus on the pattern recognition underlying a particular dataset.

Mainly, it’s a joint effort of machine learning, pattern recognition and statistics. They help in discovering patterns in data. Clustering is one of the various methods of data mining.

What is clustering in data mining?

Generally, the mining of data ends up at spotting the pattern. If you talk about clustering in particular, it’s an unsupervised data mining method that splits the data into natural groups. In other words, clustering is the statistical distribution of data into subclasses. Each subclass showcases a group of similar objects.  It’s a kind of unsupervised algorithm.

Let’s consider this example to clarify its meaning. When you type a phrase in Google, it immediately monitors. Whenever you browse it again, it lines up an array of ads that are motivated by your previous search. Its bots take a few minutes to scan what you explored. Likewise, many other users would have browsed the similar or related information. But, their phrases might differ. Its bots put billions of searches in algorithms to make a list of the most searchable phrases. It’s what the data mining is.

The unsupervised algorithms use multiple variables describing …

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