Fast Outlier Detection in High Dimensional Spaces

In this paper we propose a new definition of distance-based outlier that considers for each point the sum of the distances from its k nearest neighbors, called weight. Outliers are those points having the largest values of weight. In order to compute these weights, we find the k nearest neighbors of each point in a fast and efficient way by linearizing the search space through the Hilbert space filling curve. The algorithm consists of two phases, the first provides an approximated solution, within a small factor, after executing at most d + 1 scans of the data set with a low time complexity cost, where d is the number of dimensions of the data set. During each scan the number of points candidate to belong to the solution set is sensibly reduced. The second phase returns the exact solution by doing a single scan which examines further a little fraction of the data set. Experimental results show that the algorithm always finds the exact solution during the first phase after d ? d + 1 steps and it scales linearly both in the dimensionality and the size of the data set.