There is a mind-boggling amount of data that is being generated every day. We generate over 295 billion emails, conduct 5 billion searches and send 65 billion Whatsapp messages every day. And, as a matter of fact, this number is rising steadily.
Data mining, true to its name, digs through all this data to uncover the hidden gold or in this case, hidden insights. While there are many techniques employed by data scientists towards this end, clustering in data mining is one of the most vital methods that you should know about.
From Amazon’s Alexa to the arrangement of products in your nearest supermarket, everything can benefit from data mining techniques. So what is clustering in data mining? Why is it needed? How to do clustering? Continue reading to get answers to these pertinent questions.
What is Clustering in Data Mining?
Table of Contents
- What is Clustering in Data Mining?
- What are the Applications of Clustering in Data Mining?
- Clustering in Data Mining Methods
- Learn Clustering in Data Mining to Further Your Career
Data mining produces a tremendous number of data points. Analyzing each of them on their own will take up an enormous amount of time and resources. Many of these data points will be similar, and it would benefit you greatly to group these data points before running the analysis.
A cluster is essentially a group of objects that share some common characteristics. The process of creating such clusters is known as clustering in data mining. It identifies those objects that are similar and forms clusters. Every cluster will have elements that are more similar to each other than the ones in other clusters.
Classification and clustering are two data mining processes that are often confused with each other. Both of them involve grouping data points into groups. However, the difference is that while classification is a supervised learning technique, clustering in data mining is an unsupervised learning technique that creates groups without any prior training.
The clusters are not labeled beforehand in clustering. Another key difference is that while classification uses the knowledge from training sets to create the classes, clustering in data mining techniques has no prior training.
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What are the Applications of Clustering in Data Mining?
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