Incremental Clustering

Incremental clustering addresses the challenge of efficiently updating cluster assignments as new data points arrive in a dynamic environment, aiming to avoid recomputing clusters from scratch. Current research focuses on adapting existing algorithms like k-means, affinity propagation, and spectral clustering to handle incremental updates, often incorporating techniques like cluster consolidation, stratification, and graph-based approaches for improved efficiency and accuracy. This field is significant for handling large-scale, real-time data streams in diverse applications, including social media event detection, text analysis, and time series analysis, where efficient and adaptive clustering is crucial.