K-Means Clustering

What is K-Means Clustering?

K-Means Clustering is an unsupervised learning technique that aims to divide a given dataset into K number of non-overlapping subgroups or clusters. The algorithm iteratively assigns each data point to one of the K clusters based on the minimization of the sum of squared distances between the data points and the centroids of the clusters. This process continues until the positions of the centroids no longer change significantly, indicating that the algorithm has converged. K-Means Clustering is widely used for tasks such as customer segmentation, image compression, and anomaly detection. Its simplicity and efficiency make it a go-to method for initial exploratory data analysis, although it is sensitive to the choice of K and the initial placement of centroids.

K-Means Clustering is a popular algorithm in artificial intelligence and machine learning used for partitioning data into distinct groups based on their features.

Examples

• Customer Segmentation: Retail companies like Walmart use K-Means Clustering to segment their customers into different groups based on purchasing behavior, allowing for more targeted marketing strategies.


• Image Compression: Tech companies like Google utilize K-Means Clustering to reduce the number of colors in an image, thereby compressing the image without significantly affecting its quality.

Additional Information

• K-Means Clustering requires the user to specify the number of clusters (K) in advance, which can sometimes be challenging when the optimal number is not known.


• The algorithm is sensitive to the initial placement of the centroids, and different initializations can lead to different final clusters.