One Pass Outlier Detection for Streaming Categorical Data

Abstract. Attribute Value Frequency (AVF) is a simple yet fast and effective method for detecting outliers in categorical nominal data. Previous work has shown that AVF requires lesser processing time while maintains very good outlier detection accuracy when compared with other existing techniques. However, AVF works on static data only; this means that AVF cannot be used in data stream applications such as sensor data monitoring. In this paper, we introduce a modified version of AVF known as One Pass AVF to deal with streaming categorical data. We compare this new algorithm with AVF based on outlier detection accuracy. We also apply One Pass AVF for detecting unreliable data points (i.e., outliers) in a marine sensor data monitoring application. The proposed algorithm is experimentally shown to be as effective as AVF and yet capable of detecting outliers in streaming categorical data.

This paper will appear in IDAM 2013 (Sep 2103). Please send me an email if you need the draft version.

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