Transaction analysis assumes discrete states/transactions, in contrast to continuous observations. A major research area is intrusion detection (ID), which aims to detect attacks against information systems, in general. There are two types of ID systems, signature-based and anomaly-based, that broadly follow the suspicious and anomalous pattern detection that was described in the previous sections. A comprehensive review of ID approaches was published by Gyanchandani, et al. (2012).

Furthermore, applications in ambient assisted living that are based on wearable sensors also fit to transaction analysis as sensing is typically event-based. Lymberopoulos, et al. (2008) proposed a system for automatic extraction of the user's spatio-temporal patterns, encoded as sensor activation from the sensor network deployed inside their home. The proposed method, based on location, time, and duration, was able to extract frequent patterns using the Apriori algorithm and encode the most frequent patterns in the form of a Markov chain. Another area of related work includes the hidden Markov models (HMMs) that are widely used in traditional activity recognition for modeling a sequence of actions, but these topics are already out of the scope of this book.