Clustering Analysis
Clustering analysis is an unsupervised machine learning technique used to group similar data points into clusters based on their features, without predefined labels. It helps identify patterns, structures, and relationships within datasets by partitioning them into subsets where points in the same cluster are more alike than those in different clusters. Common applications include customer segmentation, anomaly detection, and image recognition.
Developers should learn clustering analysis when working with unlabeled data to discover hidden patterns or for exploratory data analysis, such as in marketing analytics to segment users or in bioinformatics to classify genes. It's essential for tasks requiring data grouping without prior knowledge, like recommendation systems or fraud detection, where it can identify outliers or similar behaviors.