To enhance PCMC's novelty detection and clustering mechanisms for more complex and diverse data streams, several strategies could be implemented:
Dynamic Threshold Adjustment: Currently, PCMC uses a fixed novelty detection threshold based on a high percentile of distance distributions. This could be improved by employing a dynamic threshold that adapts based on the characteristics of incoming data. For instance, using a moving average or exponential smoothing of distances could help in adjusting the threshold in real-time, allowing the model to be more sensitive to changes in data distribution.
Hierarchical Clustering: Implementing a hierarchical clustering approach could allow PCMC to better manage the relationships between clusters. By organizing clusters into a hierarchy, the model could identify sub-clusters within larger clusters, which would be particularly useful in complex data streams where classes may share features or exhibit overlapping characteristics.
Incorporation of Temporal Information: Adding a temporal dimension to the clustering process could help in understanding how classes evolve over time. By considering the sequence of incoming data, PCMC could leverage temporal patterns to improve novelty detection, allowing it to recognize when a class is becoming more prominent or when a new class is emerging.
Multi-Modal Data Handling: To better accommodate diverse data streams, PCMC could be extended to handle multi-modal inputs (e.g., images, text, audio). This could involve developing specialized encoders for different modalities and integrating their outputs into a unified clustering framework, thus enhancing the model's ability to learn from varied data types.
Enhanced Feature Representation: Utilizing advanced feature extraction techniques, such as attention mechanisms or graph-based representations, could improve the quality of embeddings generated from patches. This would allow for more nuanced clustering and better differentiation between similar classes.