Cluster Analysis
Cluster analysis is an unsupervised machine learning technique used to group similar data points into clusters based on their characteristics, without prior knowledge of the groups. It helps identify patterns, structures, and relationships within datasets by minimizing intra-cluster distances and maximizing inter-cluster distances. Common applications include customer segmentation, image recognition, and anomaly detection.
Developers should learn cluster analysis when working with large, unlabeled datasets to discover hidden patterns or group similar items, such as in market research for customer segmentation or in bioinformatics for gene expression analysis. It is essential for exploratory data analysis, data preprocessing, and building recommendation systems, as it provides insights that can inform decision-making and improve model performance in machine learning pipelines.