How does streaming clustering work in real-time systems?

Streaming clustering processes data incrementally instead of batch training using online updates.

In real-time systems, data arrives continuously and cannot be stored entirely. Streaming clustering algorithms like Online K-Means update centroids incrementally as new data points arrive. This avoids retraining from scratch. Challenges include concept drift, memory constraints, and maintaining cluster stability over time.