Distributed Strategies for Mining Outliers in Large Data Sets

In this work we introduce a distributed method for detecting distance-based outliers in very large data sets. Our approach is based on the concept of outlier detection solving set [2], which is a small subset of the data set that can be also employed for predicting novel outliers. The method exploits parallel computation in order to obtain vast time savings. Indeed, beyond preserving the correctness of the result, the proposed schema exhibits excellent performances. From the theoretical point of view, for common settings, the temporal cost of our algorithm is expected to be at least three order of magnitude faster than the classical nested-loop like approach to detect outliers. Experimental results show that the algorithm is efficient and that its running time scales quite well for increasing number of nodes. We discuss also a variant of the basic strategy which reduces the amount of data to be transferred in order to improve both the communication cost and the overall run time. Importantly, the solving set computed by our approach in distributed environment has the same quality as that produced by the corresponding centralized method