A caveat about K-means clustering and many other machine learning models: Stochasticity

Your amazing data scientist has performed K-means clustering to segment customers, and the clusters made sense and you could label them accordingly.

But then, she runs the model again and the clusters look completely different!

You are confused, concerned, or even maybe disappointed. Please don’t be!

Stochasticity is an inherent part of some machine learning models, including K-means clustering.

K-means is one of the most widely used algorithms in data science due to its simplicity and efficiency in partitioning data into distinct clusters. It aims to partition data into k clusters by minimizing the within-cluster variance.

One of the key reasons for the different results is the random initial placement of centroids (the center point of the clusters).

Random initialization may cause the algorithm to converge to local minima, resulting in suboptimal clustering. This randomness can be especially problematic when the data has overlapping clusters or outliers, and this variability is a significant caveat for applications requiring high reliability and reproducibility.

To illustrate this, I ran a simple experiment by running K-means clustering on the same synthetic dataset multiple times. The results showed more than one clustering outcome, as shown in the image below.

In the case of K-means clustering, there are effective alternative solutions to limit stochasticity if needed.

One solution is using the K-means++ initialization method, which strategically spreads out the initial centroids to improve clustering results. Another approach is running the K-means algorithm multiple times with different random initializations and choosing the clustering solution that has the lowest sum of squared distances from points to their assigned cluster centers.

However, some machine learning and AI models inherently include stochasticity that cannot be completely removed.

In such cases, being aware of the potential fluctuations and planning for them in your analysis and decision-making processes is essential.

➡ It’s crucial to develop strategies to manage this variability. This might involve setting expectations for variability, conducting multiple runs to understand the range of possible outcomes, and designing robust models that can accommodate and adjust to the inherent randomness in the data.