Reducing distance computations for distance-based outliers

The mining task of outlier detection is essential in many expert and intelligent systems exploited in a wide range of applications, from intrusion detection to molecular biology. In some of such applications the ability to process large amounts of data in a very short time can be critical, for instance in intrusion and fraud detection. This paper explores a solution for the optimisation of an exact, unsupervised outlier detection method by avoiding unnecessary computations, and therefore reducing the running time and making the method usable also in settings where response times are crucial. In particular, we enhance the SolvingSet-based approach by using a mechanism that exploits the knowledge learned during the algorithm execution and avoids a large amount of distance computations. We demonstrate the strength of the proposed solution, named FastSolvingSet, through both theoretical and experimental analysis.