Which of the following is a common application of K-Means clustering?Answer areaImage compressionPredicting stock pricesSpam filteringSentiment analysis

K-Means clustering is a popular unsupervised machine learning algorithm that is widely used for various applications in data analysis. Among the options provided, the most common application of K-Means clustering is image compression.

Explanation:

1. Image Compression: K-Means clustering is often employed to reduce the number of colors in an image, which effectively compresses the image size while retaining its visual fidelity. The algorithm clusters similar colors together, allowing for a crucial reduction in the palette used.

2. Predicting Stock Prices: This task typically requires supervised learning techniques, as stock price prediction is largely dependent on past data and trends rather than merely clustering.

3. Spam Filtering: While clustering can play a role here, spam filtering often involves classification algorithms that require labeled training sets.

4. Sentiment Analysis: This process generally relies on natural language processing and supervised learning to classify sentiments, rather than merely clustering.

In summary, K-Means clustering is best suited for applications where it is necessary to group similar items together, making image compression the correct choice from the given list.