Journal
MATHEMATICS AND COMPUTERS IN SIMULATION
Volume 80, Issue 9, Pages 1911-1921Publisher
ELSEVIER
DOI: 10.1016/j.matcom.2010.02.007
Keywords
Pattern recognition; Outlier detection; Semi-supervised learning; Rough sets; Fuzzy sets; C-means clustering
Categories
Funding
- National Natural Science of China [10971053]
Ask authors/readers for more resources
This paper presents a fuzzy rough semi-supervised outlier detection (FRSSOD) approach with the help of some labeled samples and fuzzy rough C-means clustering. This method introduces an objective function, which minimizes the sum squared error of clustering results and the deviation from known labeled examples as well as the number of outliers. Each cluster is represented by a center, a crisp lower approximation and a fuzzy boundary by using fuzzy rough C-means clustering and only those points located in boundary can be further discussed the possibility to be reassigned as outliers. As a result, this method can obtain better clustering results for normal points and better accuracy for outlier detection. Experiment results show that the proposed method, on average, keep, or improve the detection precision and reduce false alarm rate as well as reduce the number of candidate outliers to be discussed. (C) 2010 IMACS. Published by Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available