What is Clustering?

In your future career as a data scientist, you’ll use something called clustering to analyze data. Clustering is a data mining technique that helps you segment customers and detect data anomalies, so it’s essential you learn how to do it properly. But what is clustering, exactly? Learn the definition of this term below.

Clustering Meaning

So, what is clustering? In the simplest terms, it’s a technique based on machine learning that organizes data points into specific groups, or “clusters.” Each cluster has data points with similar features and properties, while different clusters have data points with dissimilar features and properties. Clustering is a type of unsupervised machine learning, which uses algorithms to analyze unlabeled data sets. 

While clustering is a modern machine learning technique, the act of clustering data dates back to the 1930s, when cluster analysis was a popular method in anthropology and psychology research. Today, data scientists use clustering to make sense of data points and solve data challenges for the companies they work for. You’ll almost certainly use this method in your future career. 

You’ll learn about clustering in TDI’s Data Science Bootcamp and Data Science Essentials. Both programs help you become a better data scientist and you’ll work with world-class tutors and solve real-world data challenges. Discover more about TDI’s data science programs.

How Does Clustering Work?

Clustering divides data into different clusters. Each cluster contains data with high and low inter-class similarity, which refers to how similar data points in clusters are. Before clustering, data points are disconnected from one another, with each one considered its own cluster. During clustering, the two closest data points are connected to create a cluster and then the next two closest data points are connected to create a larger cluster and so on.

The above is a basic definition of clustering and you’ll learn much more about this topic in a data science program like Data Science Essentials. However, note there are multiple clustering techniques that you might use as a data scientist. These techniques include:

• Hierarchical clustering
• Partitional clustering
• Density-based clustering
• Constraint-based clustering
• Model-based clustering

Learn the answer to the question, “What is clustering?” when enrolling in a data science program from TDI. You can develop data science skills in as little as eight weeks on Data Science Essentials, or you can learn more advanced concepts on Data Science Bootcamp. Check out TDI’s programs!

The Benefits of Clustering

Here are some of the advantages of clustering:

Segmentation

Segmentation in data analysis is one of the best use cases for clustering. For example, you can segment audiences based on common characteristics like purchasing habits and then send thoseaudiences personalized marketing materials. 

Identify Patterns and Trends

Use clustering to identify data patterns and trends to take a deeper dive into your data. These insights can help your company make more informed decisions for growth.

Improve Data Visualization

By clustering data points, it’s easier to visualize data sets on reports and dashboards. Visualizing data in this way can help your company make better decisions. 

The Drawbacks of Clustering

Disadvantages of clustering include:

• Some clustering methods require expensive resources to generate the results you need. Hierarchical clustering, for example, relies on a great deal of computational power.
• Clustering can sometimes be sensitive to data outliers, which can skew your data analysis. 
• Clustering is unlikely to result in successful data analysis on its own. You need to use other machine learning and data mining techniques to generate the intelligence you need for successful data science projects.