Trajectory outlier detection: New problems and solutions for smart cities

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for the first problem by adapting DBSCAN, k nearest neighbors (kNN), and feature selection (FS). DBSCAN-GTO first applies DBSCAN to derive the micro clusters, which are considered as potential candidates. A pruning strategy based on density computation measure is then suggested to find the group of trajectory outliers. kNN-GTO recursively derives the trajectory candidates from the individual trajectory outliers and prunes them based on their density. The overall process is repeated for all individual trajectory outliers. FS-GTO considers the set of individual trajectory outliers as the set of all features, while the FS process is used to retrieve the group of trajectory outliers. The proposed algorithms are improved by incorporating ensemble learning and high-performance computing during the detection process. Moreover, we propose a general two-phase-based algorithm for detecting the deviation points, as well as a version for graphic processing units implementation using sliding windows. Experiments on a real trajectory dataset have been carried out to demonstrate the performance of the proposed approaches. The results show that they can efficiently identify useful patterns represented by group of trajectory outliers, deviation points, and that they outperform the baseline group detection algorithms

Fast outlier detection using a GPU

The availability of cost-effective data collections and storage hardware has allowed organizations to accumulate very large data sets, which are a potential source of previously unknown valuable information. The process of discovering interesting patterns in such large data sets is referred to as data mining. Outlier detection is a data mining task consisting in the discovery of observations which deviate substantially from the rest of the data, and has many important practical applications. Outlier detection in very large data sets is however computationally very demanding and currently requires highperformance computing facilities. We propose a family of parallel algorithms for Graphic Processing Units (GPU), derived from two distance-based outlier detection algorithms: the BruteForce and the SolvingSet. We analyze their performance with an extensive set of experiments, comparing the GPU implementations with the base CPU versions and obtaining significant speedups