What  is an application of K-Means clustering?Answer areaPredicting stock pricesCustomer segmentationSentiment analysisReal-time prediction

Applications of K-Means Clustering

K-Means clustering is a versatile technique used in various fields for different purposes. Here are several applications:

1. Customer Segmentation: This is one of the most common applications of K-Means clustering. Businesses often utilize this method to divide their customer base into distinct groups based on various attributes such as purchasing behavior, demographics, and preferences. This segmentation helps in tailoring marketing strategies and improving customer service.

2. Image Compression: K-Means is also used in image processing for compressing images. By clustering similar colors into a limited number of colors, it reduces the amount of data needed to represent the image while maintaining its quality.

3. Market Basket Analysis: Retailers can use K-Means to analyze purchasing patterns and group products that are frequently bought together, helping in promotional strategies and inventory management.

4. Anomaly Detection: K-Means can identify outliers in data by grouping normal data points together and recognizing those that do not belong to any cluster.

Among the options you provided, Customer segmentation stands out as a prominent application of K-Means clustering. It allows businesses to identify and target specific groups of customers more effectively.