An Efficient Outlier Detection Algorithm for Data Streaming
Rui Hu, Luc (Zhilu) Chen, Yiwei Wang

TL;DR
This paper introduces EILOF, a new outlier detection algorithm for data streams that improves efficiency and accuracy by computing LOF scores only for new data points, making real-time anomaly detection more feasible.
Contribution
The paper presents EILOF, an incremental LOF algorithm that reduces computation and enhances detection accuracy in streaming data environments.
Findings
EILOF outperforms ILOF as data volume increases.
EILOF significantly reduces computational costs.
EILOF improves detection accuracy with more streaming data.
Abstract
The nature of modern data is increasingly real-time, making outlier detection crucial in any data-related field, such as finance for fraud detection and healthcare for monitoring patient vitals. Traditional outlier detection methods, such as the Local Outlier Factor (LOF) algorithm, struggle with real-time data due to the need for extensive recalculations with each new data point, limiting their application in real-time environments. While the Incremental LOF (ILOF) algorithm has been developed to tackle the challenges of online anomaly detection, it remains computationally expensive when processing large streams of data points, and its detection performance may degrade after a certain threshold of points have streamed in. In this paper, we propose a novel approach to enhance the efficiency of LOF algorithms for online anomaly detection, named the Efficient Incremental LOF (EILOF)…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Data Stream Mining Techniques · Network Security and Intrusion Detection
