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Mathematical Problems in Engineering
Volume 2016, Article ID 6394253, 14 pages
Research Article

An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

1College of Science, Harbin Engineering University, Harbin 150001, China
2College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China

Received 13 September 2016; Revised 2 November 2016; Accepted 20 November 2016

Academic Editor: Filippo Ubertini

Copyright © 2016 Tingquan Deng and Jinhong Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.