onlineklusterointimenetelmiä

Online clustering methods are a class of algorithms designed to group data points in real-time as they arrive, without needing to store the entire dataset. This contrasts with traditional batch clustering methods, which require all data to be available beforehand. Online clustering is particularly useful for applications where data streams are continuous and the volume is too large for memory, such as sensor networks, financial transaction monitoring, or web usage analysis.

These methods typically maintain a set of cluster representatives, often called centroids or prototypes. When a new data point arrives, it is compared to the existing cluster representatives. If it is sufficiently close to a representative, it is assigned to that cluster, and the representative is updated to reflect the new data point's characteristics. If the new point is too far from any existing representative, it may either form a new cluster or be discarded, depending on the algorithm's design and predefined parameters like the maximum number of clusters.

Key challenges in online clustering include handling concept drift, where the underlying data distribution changes over time, and deciding when to merge or split clusters. Algorithms often employ techniques like adaptive learning rates to adjust cluster representatives dynamically. The choice of distance metric and the parameters governing cluster formation and maintenance are crucial for the effectiveness of online clustering methods. Examples of online clustering algorithms include incremental K-means, BIRCH, and CluStream.