@inproceedings{4f80a20f858649beaed95f24b3d15ffe,
title = "Density-based local outlier detection on uncertain data",
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.",
author = "Keyan Cao and Lingxu Shi and Guoren Wang and Donghong Han and Mei Bai",
year = "2014",
doi = "10.1007/978-3-319-08010-9\_9",
language = "English",
isbn = "9783319080093",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "67--71",
booktitle = "Web-Age Information Management - 15th International Conference, WAIM 2014, Proceedings",
address = "Germany",
note = "15th International Conference on Web-Age Information Management, WAIM 2014 ; Conference date: 16-06-2014 Through 18-06-2014",
}