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Title: A fast MST-inspired kNN-based outlier detection method

Citation Type: Journal Article

Publication Year: 2015

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Abstract: Today׳s real-world databases typically contain millions of items with many thousands of fields. As a result, traditional distribution-based outlier detection techniques have more and more restricted capabilities and novel k-nearest neighbors based approaches have become more and more popular. However, the problems with these k-nearest neighbors based methods are that they are very sensitive to the value of k, may have different rankings for top n outliers, are very computationally expensive for large datasets, and doubts exist in general whether they would work well for high dimensional datasets. To partially circumvent these problems, we propose in this paper a new global outlier factor and a new local outlier factor and an efficient outlier detection algorithm developed upon them that is easy to implement and can provide competing performances with existing solutions. Experiments performed on both synthetic and real data sets demonstrate the efficacy of our method.

Url: https://www.sciencedirect.com/science/article/abs/pii/S0306437914001331

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Authors: Wang, Xiaochun; Wilkes, D Mitchell; Li Wang, Xia

Periodical (Full): Information Systems

Issue:

Volume: 48

Pages: 89-112

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States