Online Clustering

Online clustering focuses on dynamically grouping data points as they arrive, without requiring the entire dataset beforehand. Current research emphasizes developing robust and efficient algorithms, often integrating deep learning models (like restricted Boltzmann machines or contrastive learning networks) with online clustering techniques (such as k-means or probability aggregation clustering) to handle high-dimensional data streams and address challenges like cluster collapse and misspecified models. These advancements are significant for various applications, including malware analysis, speaker diarization, and activity recognition, enabling real-time processing and improved adaptability to evolving data patterns.