Association rule generation
In our approach we employed Association Rule Mining to discover a set of frequent patterns expressed as association rules describing the relationship between properties of PPI interaction sites and PPI types. Association rules have the form R: X → Y [c, s], where X and Y are the body and the head of the rule respectively. X and Y are disjoint predicates (X ∩ Y = ϕ). Each X and Y consists of a conjunction of distinct predicates which describe properties related to interaction sites. Note that we can consider a conjunction as a set for our purposes. In our approach, the heads of all rules Y are restricted to be one of the PPI types considered which are the target classes defined in this task. The strength of the association rules can be measured in terms of their support (s) and confidence (c). The support of a rule (X → Y) is the probability that the cases in a database contain both X and Y. The confidence of the rule is the probability that a case contains Y given that it contains X.
The generation of association rules was carried out employing the APRIORI algorithm [29]. We used the 10 g Oracle Data Miner (ODM) software which implements the APRIORI algorithm to compute the type of association rules required for our ARBC approach. We set a minimum support and confidence of 3% and 25% respectively to reduce the number of association rules generated. Association mining is not directly applicable to real valued continuous data such as some of the dom-face properties we generated. Hence we used discretisation to manipulate continuous attributes before the ARM process was executed. In this process adjacent values of continuous data were binned into a finite number of intervals.