期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 37, 期 12, 页码 8745-8749出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.06.040
关键词
Outlier detection; Rough sets; Neighborhoods; Data mining
类别
资金
- National Natural Science Foundation of China [60903203, 60775036, 60475019]
- Natural Science Foundation of Fujian Province of China [2008J0318]
KNN (k nearest neighbor) is widely discussed and applied in pattern recognition and data mining, however, as a similar outlier detection method using local information for mining a new outlier, neighborhood outlier detection, few literatures are reported on. In this paper, we introduce neighborhood model as a uniform framework to understand and implement neighborhood outlier detection. Furthermore, a neighborhood-based outlier detection algorithm is also given. This algorithm integrates rough set granular technique with outlier detecting. We propose a neighborhood-based metric on outlier detection, and compare neighborhood outlier detection with DIS, KNN and RNN. The experimental results show that neighborhood-based metric is able to measure the local information for outlier detection. The detected accuracies based on neighborhood outlier detection are superior to DIS, KNN for mixed dataset, and a litter better than RNN for discrete dataset. (C) 2010 Elsevier Ltd. All rights reserved.
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