Cluster Analysis

Cluster Analysis is an unsupervised-learning technique that groups data points so that items in the same cluster are more similar to each other than to those in other clusters. The workflow converts raw inputs—customer profiles, gene expressions, text embeddings—into numerical feature vectors, computes pairwise distances, and applies an algorithm such as K-means, Hierarchical Agglomerative, DBSCAN, or Gaussian Mixture Models to partition the space. Dimensionality reduction tools like PCA or UMAP improve speed and visualization. Analysts inspect silhouette score, Davies–Bouldin index, or elbow plots to pick the optimal cluster count and validate cohesion versus separation. Applications range from market segmentation and anomaly detection to topic mining in Retrieval-Augmented Generation pipelines. Challenges include choosing the right distance metric, handling high dimensionality, and interpreting clusters; solutions involve feature scaling, domain knowledge, and post-hoc labeling with keyword extraction or LLM summaries.