K-means Clustering

AI Terms

K-means Clustering

Definition: K-means clustering is a popular unsupervised learning algorithm used to partition data into K clusters.

Detailed Description:

K-means clustering works by iteratively assigning data points to the nearest cluster centroid and updating the centroids based on the assigned data points. The goal is to minimize the within-cluster sum of squares (WCSS).

K-means clustering is a simple and efficient algorithm, but it can be sensitive to the choice of the initial centroids and may not work well with non-spherical clusters.

Related Terms:

Clustering
Unsupervised Learning
Centroid
Within-Cluster Sum of Squares (WCSS)

FAQs:

How does K-means clustering work?
- K-means clustering initializes K random centroids, assigns data points to the nearest centroid, updates the centroids, and repeats these steps until convergence.
How do you choose the optimal number of clusters (K)?
- There are various methods to determine the optimal number of clusters, such as the elbow method and the silhouette coefficient.
What are the limitations of K-means clustering?
- K-means clustering can be sensitive to the choice of initial centroids and may not work well with non-spherical clusters.

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