Accelerating Outlier Detection with Uncertain Data Using Graphics Processors

Abstract

Outlier detection (also known as anomaly detection) is a common data mining task in which data points that lie outside expected patterns in a given dataset are identified. This is useful in areas such as fault detection, intrusion detection and in pre-processing before further analysis. There are many approaches already in use for outlier detection, typically adapting other existing data mining techniques such as cluster analysis, neural networks and classification methods such as Support Vector Machines. However, in many cases data from sources such as sensor networks can be better represented with an uncertain model. Detecting outliers with uncertain data involves far more computation as each data object is usually represented by a number of probability density functions (pdfs).

In this paper, we demonstrate an implementation of outlier detection with uncertain objects based on an existing density sampling method that we have parallelized using the cross-platform OpenCL framework. While the density sampling method is a well understood and relatively straightforward outlier detection technique, its application to uncertain data results in a much higher computational workload. Our optimized implementation uses an inexpensive GPU (Graphics Processing Unit) to greatly reduce the running time. This improvement in performance may be leveraged when attempting to detect outliers with uncertain data in time sensitive situations such as when responding to sensor failure or network intrusion.