What Is K-Means Clustering?

The k-means cluster analysis algorithm classifies information based on the similarities of the data points. As a professional, you can use this method for customer segmentation, habitat classification analysis, gene expression patterns, trend identification for prediction, and more. In this article, discover more about k-means clustering, how industry leaders use this technique, its advantages and disadvantages, and tips for implementing the procedure.

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What is k-means clustering?

K-means clustering is a popular technique that takes a pre-defined number of clusters and, using a k-means algorithm iteratively assigns a characteristic to each group until similar groupings are found. It’s a method you can use to divide a bunch of data points into distinct groups, ensuring that each point is in the group closest to it.

To conceptualize this, you have a stack of pictures you want to classify into groups.  First, determine the number of clusters (or groups) you want to create. The k-means algorithm then randomly places “centroids,” which are the “representative values” for each group. You group the pictures based on which representative value they are most similar to. You then move the centroids to the center of their respective clusters, regrouping the pictures again. This process repeats until the centroid values don’t move much anymore, meaning each image is as close as possible to its group’s centroid while being as far away from a group different than itself.

Read more: What Is Clustering?

What is k-means clustering used for?

You can apply k-means clustering across many different fields, each benefiting from the algorithm’s ability to group data into a pre-defined number of clusters. While applications vary widely depending on your needs, some common ways you can apply this method in professional fields include the following.

1. Business 

Your company can use k-means clustering to segment customers with similar behaviors or preferences to tailor marketing strategies, improve customer service, or target specific groups. It’s also used in business contexts to categorize inventory, detect abnormalities, group images, or separate audio.

2. Machine learning and artificial intelligence

In the tech industry, k-means help simplify complex data sets. The method enables machine learning models to learn and make predictions more easily. You can use it in image analysis and natural language processing and as a way to improve cluster algorithm performance. 

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3. Psychology 

Researchers apply k-means clustering to understand patterns in human behavior or social trends. For example, clustering learner characteristics can reveal performance trends and help develop prediction models for future academic performance.

4. Biology

In life sciences, k-means clustering can help group medical data, such as cancer subtypes, with similar expression patterns, which can help understand disease risk. It’s also used in ecological studies to classify similar habitats or species based on environmental data.

Pros and cons of using k-means clustering 

When choosing k-means as your clustering technique, being aware of the advantages and limitations can help ensure you select a suitable algorithm for your data and check for common pitfalls associated with this technique. When done correctly, k-means is a powerful clustering technique that has proven benefits in various applications. 

Pros

• Simplicity: K-means are easy to understand because you only need to group your data based on how similar each point is to the nearest centroid value. 

• Speed: K-means is quick and efficient. It can quickly organize your data into clusters, making it a practical choice for real-time analysis, which is especially applicable for banks because of how much new data enters their systems on a daily basis.

• Scaling to large data sets: The k-means algorithm generally scales well to larger data sets, which is beneficial when working with mass amounts of data in industries such as health care, marketing, and transportation.

• Grouping unlabeled data: For data sets without predefined labels or categories, k-means allows you to discover natural groupings. This makes it an excellent choice for exploratory analysis, where you’re trying to uncover hidden patterns without prior assumptions.

Cons

• Continuous variables required: K-means only works when all variables are continuous. It doesn’t work well with categorical data (like gender or country names), which limits its applicability to certain types of data sets.

• Selection of variables: The choice of variables significantly impacts the clustering outcome. You must carefully select variables to represent the underlying data distribution. To do this well, you need a solid understanding of your data set and the goals of the analysis. You may also need to normalize your variables before beginning, which can impact results in some instances.

• Need to run several times: The algorithm finds a “local optimum,” meaning findings depend on the placement of the random starting centroid points. To ensure accurate results, you need to re-run the program several times to validate your findings.

• Number of clusters is subjective: When determining the correct number of clusters before initiating the k-means algorithm, you need to choose based on subject matter knowledge and the structure of your data. Because it is a subjective choice, you could potentially end up with errors in some instances. You may need to run the algorithm several times using different numbers of clusters before discovering the number that works best. 

• Sensitive to outliers: The k-means algorithm is sensitive to outliers, meaning the results may be less accurate if your data has high variability.

How to implement your own k-means clustering algorithm

To start learning how to implement k-means clustering on your own, you should begin by exploring fundamentals in your programming language of choice, such as Python. You can find many pre-defined k-means algorithms in different statistical software packages, each of which can help you implement your analysis. When finding data sets, look for sample projects based on your interests. Try to cluster different data types (e.g., customer data, biological data) to get experience applying k-means to various data sets. 

Some software you can begin with includes:

• R: Try the “cluster” package with the “k-means” function.

• STATA: Try the “cluster kmeans” command.

• SAS: Try the “PROC FASTCLUS” procedure.

• SPSS: Try the Analyze / Classify / K-Mean Cluster function.

How to get started in k-means clustering

If you’re interested in k-means clustering, consider entering the field of data science. As a data scientist working for a company, you would use the algorithm to gain insightful information from large data sets. These insights could help the company improve marketing, customer service, and decision-making. 

To become a data scientist, you usually need to get your bachelor’s degree in an area of study like statistics, computer science, or mathematics. Some companies prefer or even require a master’s. If you select this career option, your job prospects will likely be positive because the US Bureau of Labor Statistics (BLS) predicts that the job outlook for data scientists will grow 35 percent from 2022 to 2032, a rate that's significantly faster than the average across all professions [1]. According to Lightcast™, data scientists in the US can earn an annual salary of $114,282.59 [2].

Getting started with Coursera

If you want to continue learning about clustering algorithms and data analysis techniques, consider taking courses on Coursera. For beginners, take the Machine Learning Specialization offered by Stanford, which helps learners build background skills in various machine learning topics. For intermediate-level learners, the IBM Machine Learning Professional Certificate offers a slightly more advanced look at topics such as machine learning algorithms, human learning, and data analysis techniques. Upon completing either program, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.