Mining Frequent Patterns in Data Streams at Multiple Time Granularities

TLDR

Frequent‑pattern mining is well established, but extending it to data streams is difficult because streams contain more information, higher complexity, evolving item frequencies, and require dynamic storage structures. The study proposes to compute and maintain all frequent patterns and update them dynamically as new data streams arrive. The authors extend the framework to mine time‑sensitive patterns with approximate support guarantees and incrementally maintain tilted‑time windows for each pattern at multiple time granularities. Interesting.

Abstract

Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time. In this paper, we propose computing and maintaining all the frequent patterns (which is usually more stable and smaller than the streaming data) and dynamically updating them with the incoming data streams. We extended the framework to mine time-sensitive patterns with approximate support guarantee. We incrementally maintain tilted-time windows for each pattern at multiple time granularities. Interesting