Finding High Order Dependencies in Data

We propose DepMiner, a method implementing a simple but effective model for the evaluation of the high-order dependencies in a set S of observations. S can be either ordered — thus forming a sequence of events — or not. DepMiner is based on Delta, a measure of the degree of surprise of S based on the departure of the probability of S from a referential probability estimated in the condition of maximum entropy. The method is powerful: at the same time it detects significant positive dependencies as well as negative ones suitable to identify rare events. The system returns the patterns ranked by Delta; they are guaranteed to be statistically significant and their number results reduced in comparison with other methods.