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How To Choose The Right Clustering Algorithm For Your Business Dataset

How To Choose The Right Clustering Algorithm For Your Business Dataset

As a business, you use and rely on a lot of data to run your operations. For this data to be valuable, you must analyse and understand it; otherwise, you can make wrong decisions for your business.

With a large amount of data, manually analysing it can be a complex task, not forgetting the possibility of making errors. Fortunately, you can use clustering algorithms to arrange and analyse your data, enabling you to make crucial business decisions.

By definition, a clustering algorithm aims to find similarities in your data and group them accordingly into clusters. It does this through machine learning. With the different clustering algorithms to choose from, how will you know the right one for your business data set?

Here’s a guide to follow:

Know The Types Of Clustering Algorithms

As stated earlier, many algorithms exist. The only way to choose among them is to know which ones exist and what they do. With this information, you can weigh if they suit your business dataset.

Hierarchical clustering is one of the algorithms you can consider. As the name suggests, it’ll arrange your data based on hierarchy, which depends on your command. Further, it’ll either use a bottom-up or top-down strategy to analyse your data. The bottom-up technique is where it groups each data point; whereas the top-down separates all the data points by applying principle component analysis. Generally, a hierarchical algorithm works to find the distance between objects, which later helps group them.

The other algorithm is the centroid-based technique. It classifies your data into clusters, often categories, such as gender and age. The distribution algorithm looks for similarities between data sets and given groupings. For instance, it can tell you that a given dataset falls under a given age group.

Lastly, there’s the density-based clustering algorithm. It helps you analyse data through spheres. Once it groups clusters accordingly, the data sets that fall within a circle are said to be similar, with those falling outside dissimilar.

The above is just a snippet of the available clustering algorithms. It’s best to find online resources for detailed information about each algorithm.

Check Your Type And Size Of Data

Clustering algorithms have different capabilities; hence you must use the appropriate one for your data. One way of ensuring this is by checking your type of data. Is it clean, or does it require sorting before analysis? Is the data categorically based or elementally based?

If your data isn’t clean, adopt the density-based algorithm. It works best with datasets that don’t make sense, arranging them accordingly to enable you to analyse them better.

When it comes to size, you want to factor in time. As a business, you don’t have much time to focus on one task that takes ages to execute. It’ll lead to downtime and unproductivity, negatively affecting your returns and profits. If you have large data, it’s best to go for an algorithm that breaks them down in no time. Here, the best technique to adopt is hierarchical clustering. It’ll group your dataset within no time despite its size.

Look At Your Business Goals

Goals are the cornerstone of any business. They direct each operation, in this case, data analysis. Your goals can guide you in choosing the suitable clustering algorithm to adopt. How?

Suppose you want to make predictions from the dataset you have. It’s best to adopt density-based clustering. This type of clustering is ideal in marketing as it enables you to identify the products to focus on based on the number of customers falling within the product’s sphere. With this information, your team of machine learning experts will advise you on where to focus your resources to prevent waste and enhance customer satisfaction.

A particular aspect influences your business goals, making it essential as you decide on the clustering algorithm to adopt. This aspect is the niche in which your business falls. For instance, the hierarchical algorithm is best for the research and development niche. Since you’ll be dealing with random datasets you collect from patients, this algorithm will help you decipher everything within the shortest time possible.

The purpose of factoring in your niche as you decide on a clustering algorithm is to end up with clusters and data you can understand that relate to your business. It’s the only way the algorithms will be valuable to you and your business.


Finding the suitable clustering algorithm for your business dataset isn’t as challenging as you imagined. With the right information, the process is easy despite the technicalities involved with this kind of data handling. This article is a simple but constructive guide to assist you in your selection process. Be sure to implement this guide for successful business processes, specifically analysis.

Written by Marcus Richards

Photo by sol on Unsplash

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