Journal
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
Volume 11, Issue 1, Pages 652-662Publisher
SPRINGERNATURE
DOI: 10.2991/ijcis.11.1.50
Keywords
data mining; outlier detection; high-dimensional data; evaluation measurement
Categories
Funding
- NSF of Zhejiang Province [LGG18F020017, LY18F020019, LZ14F030001]
- National Science Foundation (NSF) of China [61572443, 61672467]
- Shanghai Key Laboratory of Intelligent Information Processing [IIPL-2016-001]
- fund of Chongqing [CSTC2017ZDCY-ZDYF0366, YJG152002]
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Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly. Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields, including medical health, credit card fraud and intrusion detection. They can be used for conventional data analysis. However, it is not a trivial work to identify rare behaviors or patterns out from complicated data. In this paper, we provide a brief overview of the outlier detection methods for high-dimensional data, and offer comprehensive understanding of the-state-of-the-art techniques of outlier detection for practitioners. Specifically, we firstly summarize the recent advances on outlier detection for high-dimensional data, and then make an extensive experimental comparison to the popular detection methods on public datasets. Finally, several challenging issues and future research directions are discussed.
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