Efficient periodicity mining of sequential patterns in a post-mining environment

摘要

Sequential pattern mining approaches mainly deal with finding the positive behaviour of a sequential pattern that can help in predicting the next event after a sequence of events. In addition, sequential patterns may exhibit periodicity as well, i.e. during weekends 80% of people who watch a movie in cinemas will have a meal in a restaurant afterwards. This is a problem that has not been studied in the literature. To confront the problem of discovering periodicity for sequential patterns we adopt and extend a periodic pattern mining approach which has been utilised in association rule mining. However, due to the sequential/temporal nature of sequential patterns, the process of finding the periodicity of a given sequential pattern increases the complexity of the above mentioned association rule mining approach considerably. As a key attribute of any data mining strategy we provide a comprehensive and flexible problem definition framework for the above mentioned problem. Two main mining techniques are introduced to facilitate the mining process. The Interval Validation Process (IVP) is introduced to neutralise complexities which emerge due to the temporal/sequential nature of sequential patterns, whereas the Process Switching Mechanism (PSM) is devised to increase the efficiency of the mining process by only scanning relevant data-sets from the source database. The approach proposed in this paper is based on a post-mining environment, where the identification of sequential patterns from a database has already taken place.