Extending the state-of-the-art of constraint-based pattern discovery

In the last years, in the context of the constraint-based pattern discovery paradigm, properties of constraints have been studied comprehensively and on the basis of this properties, efficient constraint-pushing techniques have been defined. In this paper we review and extend the state-of-the-art of the constraints that can be pushed in a frequent pattern computation. We introduce novel data reduction techniques which are able to exploit convertible anti-monotone constraints (e.g., constraints on average or median) as well as tougher constraints (e.g., constraints on variance or standard deviation). A thorough experimental study is performed and it confirms that our framework outperforms previous algorithms for convertible constraints, and exploit the tougher ones with the same effectiveness.Finally, we highlight that the main advantage of our approach, i.e., pushing constraints by means of data reduction in a level-wise framework, is that different properties of different constraints can be exploited all together, and the total benefit is always greater than the sum of the individual benefits. This consideration leads to the definition of a general Apriori-like algorithm which is able to exploit all possible kinds of constraints studied so far.