Application of computational verb theory to association rule mining

There are many algorithms for association rule mining, but in practice we usually face raw data that is inappropriate for these algorithms because of lacking a unified preprocessing framework. In this paper, a general framework for dynamic data processing is presented, which is based on computational verb theory (CVT). Linear standard computational verbs are used and computational verb similarities are employed to process raw data, such that the association rules of trends can be found. One example of time series of an Internet shop is studied to show the usefulness of the association rule mining algorithm proposed in this paper.