Data mining for imprecise temporal associations

The field of data mining is dedicated to the analysis of data in order to find underlying connections and the discovery of new patterns. Since the volume of data to be analyzed is sometimes quite significant, there is the need for efficient data mining algorithms to be implemented. The market-basket algorithm can represent a breakthrough in data mining techniques. As the associations that are to be analyzed grow more and more abstract, the market-basket approach is unable to deal with imprecise temporal associations, leaving a big area uncharted. This research is dedicated to the analysis of temporal imprecise associations through the modification of a standard a-priori approach by means of fuzzy set relations to classify the associations relating different sources of data. The results of this research show that it is possible to investigate such relations with the help of fuzzy set classification for temporal associations, and the result of such exploration is as easily understandable as the standard a-priori algorithm.