Mining follow-up correlation patterns from time-related databases
Research on traditional association rules has gained a great attention during the past decade. Generally, an association rule A -> B is used to predict that B likely occurs when A occurs. This is a kind of strong correlation, and indicates that the two events will probably happen simultaneously. However, in real world applications such as bioinformatics and medical research, there are many follow-up correlations between itemsets A and B, such as, B is likely to occur n times after A has occurred m times. That is, the correlative itemsets do not belong to the same transaction. We refer to this relation as a follow-up correlation pattern (FCP). The task of mining FCP patterns brings more challenges on efficient processing than normal pattern discovery because the number of potentially interesting patterns becomes extremely large as the length limit of transactions no longer exists. In this paper, we develop an efficient algorithm to identify FCP patterns in time-related databases. We also experimentally evaluate our approach, and provide extensive results on mining this new kind of patterns.
follow-up correlation pattern time-related database data mining correlation mining