K-Means Clustering
K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into k distinct, non-overlapping clusters, where each data point belongs to the cluster with the nearest mean (centroid).
This article will delve into the core concepts of k-means clustering, explaining how it works, its applications, and how to implement it.
| Concept | Description |
|---|---|
| Centroid | The average of all data points in a cluster. Used as the center of the cluster. |
| Iteration | The process of updating cluster assignments and centroids until convergence. |
| Convergence | When the centroids no longer change significantly. |
Example
Consider a dataset of customer data with features like age, income, and spending habits. You can use k-means clustering to segment your customers into distinct groups, allowing you to target them with tailored marketing campaigns.