K-Means Clustering is an unsupervised machine learning algorithm utilized to group semantically similar entities or keywords into clusters based on text similarity. In the clustering workflow, K-Means groups the high-dimensional feature vectors (e.g., from TF-IDF). It is a scalable and efficient method for topic modeling, especially when combined with dimensionality reduction techniques like PCA for visualization. This process can reveal patterns in search data, enabling marketers to prioritize content creation on topics that are semantically related or in close proximity to existing areas of topical authority.