K-Means Clustering

Executive Definition: K-Means Clustering — An unsupervised machine learning algorithm that partitions data into a predefined number (K) of distinct clusters.

K-Means works by assigning data points to the nearest cluster centroid and then iteratively updating the centroid positions to minimize the variance within each cluster. It is computationally efficient and widely used for tasks like vector quantization, image segmentation, and data exploration, though its performance heavily depends on the initial centroid placement and the chosen value of K.

Frequently Asked Questions

How do you determine the optimal value of K?

The most common techniques are the elbow method, which looks for a point of diminishing returns in the sum of squared errors, and silhouette analysis, which measures how similar an object is to its own cluster compared to others.

What are the limitations of K-Means?

It assumes clusters are spherical and similarly sized. It is sensitive to outliers and requires the user to manually specify the number of clusters (K) beforehand.

How does K-Means handle categorical data?

Standard K-Means uses Euclidean distance, which requires numerical data. For categorical data, variants like K-Modes or techniques like one-hot encoding are typically used.