Abstract
Outlier detection is one of the key problems in the data mining area which can reveal rare phenomena and behaviors. In this paper, we will examine the problem of density-based local outlier detection on uncertain data sets described by some discrete instances. We propose a new density-based local outlier concept based on uncertain data. In order to quickly detect outliers, an algorithm is proposed that does not require the unfolding of all possible worlds. The performance of our method is verified through a number of simulation experiments. The experimental results show that our method is an effective way to solve the problem of density-based local outlier detection on uncertain data.
This research are supported by the NSFC (Grant No. 61025007, 61328202, 61173029, 61100024, 61332006, 61073063 ), National High Technology Research and Development 863 Program of China (GrantNo.2012AA011004), National Basic Research Program of China (973, Grant No. 2011CB302200-G).
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© 2014 Springer International Publishing Switzerland
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Cao, K., Shi, L., Wang, G., Han, D., Bai, M. (2014). Density-Based Local Outlier Detection on Uncertain Data. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_9
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DOI: https://doi.org/10.1007/978-3-319-08010-9_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
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