Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering

This paper address the problem of temporal pattern mining from multiple data streams containing temporal events. Temporal events are considered as real world events aligned with comprehensive starting and ending timing information rather than simple integer timestamps. Predefined relations, such as "before" and "after", describe the heterogeneous relationships hidden in temporal data with limited diversity. In this work, the relationships among events are learned dynamically from the temporal information. Each event is treated as an object with a label and numerical attributes. An online-offline model is used as the primary structure for analyzing the evolving multiple streams. Different distance functions on temporal events and sequences can be applied depending on the application scenario. A prefix tree is introduced for a fast incremental pattern update. Events in the real world usually persist for some period. It is more natural to model events as intervals with temporal information rather than as points on the timeline. Based on the representation proposed in this work, our approach can also be extended to handle interval data. Experiments show how the method, with richer information and more accurate results than the state-of-the-art, processes both point-based and interval-based event streams efficiently.