A development framework for temporal data mining

Data mining is a field which potentially offers nonexplicitly stored knowledge for a particular application domain. In most application areas that have been studied for data mining, the time at which something happened is also known and recorded (e.g. the date and time when a point-of-sale transaction took place, or a patient's temperature was taken). Most existing approaches, however, take a static view of an application domain so that the discovered knowledge is considered to be valid indefinitely on the time line. If data mining is to be used as a vehicle for better decision making, the existing approaches will in most cases lead into not very significant or interesting results. Consider, for example, a possible association between butter and bread (i.e. people who buy butter also buy bread) among the transactions of a supermarket. If someone looks at all transactions that are available, say for the past ten years, that association might be-with a certain possibility-true. If, however, the highest concentration of people who bought butter and bread can be found up to five years ago, then the discovery of the association is not significant for the present and the future of the supermarket framework, an SQL-like mining language is also proposed. With this language, any temporal data-mining task can easily be expressed.