Mining temporal features in association rules

Abstract

In real world applications, the knowledge that is used for aiding decision-making is always time-varying. However, most of the existing data mining approaches rely on the assumption that discovered knowledge is valid indefinitely. People who expect to use the discovered knowledge may not know when it became valid, or whether it still is valid in the present, or if it will be valid sometime in the future. For supporting better decision making, it is desirable to be able to actually identify the temporal features with the interesting patterns or rules. The major concerns in this paper are the identification of the valid period and periodicity of patterns and more specifically association rules.