Temporal association rule mining: An overview considering the time variable as an integral or implied component

Abstract

Association rules are commonly used to provide decision-makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real-life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well-defined framework that allows researchers both to easily locate the previous proposals and to propose well-grounded methods in the future. To accomplish both objectives, a two-level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed.