New frequent pattern mining algorithm tested for activities models creation

Abstract

When extracting frequent patterns, usually, the events order is either ignored or handled with a simple precedence relation between instants. In this paper we propose an algorithm applicable when perfect order, between events, must be respected. Not only it estimates delay between two adjacent events, but its first part allows non temporal algorithms to work on temporal databases and reduces the complexity of dealing with temporal data for the others. The algorithm has been implemented to address the problem of activities models creation, the first step in activity recognition process, from sensors history log recorded in a smart home. Experiments, on synthetic data and on real smart home sensors log, have proven the algorithm effectiveness in detecting all frequent activities in an efficient time.