Mining Temporal Relationships with Multiple Granularities in Time Sequences
Cláudio Bettini, Xiaoyang Sean Wang, Sushil Jajodia · 1998
This paper reports the progress in this front. A more detailed study can be found in [4]. In this paper, we focus on algorithms for discovering sequential relationships when a rough pattern of relationships is given. The rough pattern (which we term "event structure") specifies what sort of relationships a user is interested in. For example, a user may be interested in "which pairs of events occur frequently one week after another". The algorithms will find the instances that fit the event structure. They can also be used when more information is filled in the structure, as in in the following example from the stock market domain: "what kind of events frequently follow within 5 trading-days from the occurrence of a rise in IBM stocks, when this is preceded, one week before, by the fall of another hi-tech company stock". These algorithms form a core component for a data mining environment. We view the actual data mining process as being done interactively through a user interface. Data mining requests (in terms of event structures) are issued through the user interface and processed using the algorithms. Initially, the user will issue a request with a simple structure. Complicated structures may be given by the user