A Grid Partition-Based Local Outlier Factor for Data Stream Processing

Outlier detection is getting significant attention in the research field of big data. Detecting the outlier is important in various applications such as communication, finance, fraud detection, and network intrusion detection. Data streams posed new challenges to the existing algorithms of outlier detection. Local Outlier Factor (LOF) is one of the most appropriate techniques used in the density-based method to determine the outlier. However, it faces some difficulties regarding data streams. First, LOF processes the data all at once, which is not suitable for data streams. Another issue appears when a new data point arrives. All the data points need to be recalculated again significantly. Therefore, it affects the execution time. A new algorithm is proposed in this research paper called Grid Partition-based Local Outlier Factor (GP-LOF). GP-LOF uses a grid for the LOF with a sliding window to detect outliers. The outcome of experiments with the proposed algorithm demonstrates the effectiveness in both performance accuracy and execution time in several real-world datasets compared to the state-of-the-art DILOF algorithm.