Rough-based semi-supervised outlier detection

Authors:
Zhenxia Xue;Sanyang Liu
Affiliations:
School of Science, Henan University of Science and Technology, Luoyang, China and School of Science, Xidian University, Xi'an, China;School of Science, Xidian University, Xi'an, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

Citing 6
Cited 0

LOF: identifying density-based local outliers

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining top-n local outliers in large databases

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Interval Set Clustering of Web Users with Rough K-Means

Journal of Intelligent Information Systems
Semi-Supervised Adapted HMMs for Unusual Event Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Semi-supervised outlier detection

Proceedings of the 2006 ACM symposium on Applied computing
A Neural Network-Based Novelty Detector for Image Sequence Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the help of some labeled samples and rough C-means clustering, a rough-based semi-supervised outlier detection (RBSSOD) is proposed, which integrates the advantage of semi-supervised outlier detection (SSOD) and rough C-means clustering. This method takes into account the information of labeled points, as well as the points located in boundary area of each cluster, which can be further discussed the possibility to be reassigned as outliers. Experiment results show that our method not only keep, or improve precision and false alarm rate but also speed up the learning process.