Title
A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes
Abbreviated Journal Title
Data Min. Knowl. Discov.
Keywords
Outlier detection; Anomaly detection; Data mining; Distributed data; sets; Mixed attribute data sets; High-dimensional data sets; LOCAL OUTLIERS; Computer Science, Artificial Intelligence; Computer Science, Information; Systems
Abstract
Outlier detection has attracted substantial attention in many applications and research areas; some of the most prominent applications are network intrusion detection or credit card fraud detection. Many of the existing approaches are based on calculating distances among the points in the dataset. These approaches cannot easily adapt to current datasets that usually contain a mix of categorical and continuous attributes, and may be distributed among different geographical locations. In addition, current datasets usually have a large number of dimensions. These datasets tend to be sparse, and traditional concepts such as Euclidean distance or nearest neighbor become unsuitable. We propose a fast distributed outlier detection strategy intended for datasets containing mixed attributes. The proposed method takes into consideration the sparseness of the dataset, and is experimentally shown to be highly scalable with the number of points and the number of attributes in the dataset. Experimental results show that the proposed outlier detection method compares very favorably with other state-of-the art outlier detection strategies proposed in the literature and that the speedup achieved by its distributed version is very close to linear.
Journal Title
Data Mining and Knowledge Discovery
Volume
20
Issue/Number
2
Publication Date
1-1-2010
Document Type
Article
Language
English
First Page
259
Last Page
289
WOS Identifier
ISSN
1384-5810
Recommended Citation
"A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes" (2010). Faculty Bibliography 2010s. 382.
https://stars.library.ucf.edu/facultybib2010/382
Comments
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