Temporal sequence associations for rare events

Abstract

In many real world applications, systematic analysis of rare events, such as credit card frauds and adverse drug reactions, is very important. Their low occurrence rate in large databases often makes it difficult to identify the risk factors from straightforward application of associations and sequential pattern discovery. In this paper we introduce a heuristic to guide the search for interesting patterns associated with rare events from large temporal event sequences. Our approach combines association and sequential pattern discovery with a measure of risk borrowed from epidemiology to assess the interestingness of the discovered patterns. In the experiments, we successfully identify a known drug and several new drug combinations with high risk of adverse reactions. The approach is also applicable to other applications where rare events are of primary interest.