Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 84, Issue -, Pages 272-280Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2017.05.009
Keywords
Local outlier; Attribute relevance analysis; Particle swarm optimization; Sparse coefficient; Sparse factor
Categories
Funding
- National Natural Science Foundation of P. R. China [61572343]
- U.S. National Science Foundation [CCF-0845257(CAREER)]
Ask authors/readers for more resources
In this study, we propose a novel local outlier detection approach - called LOMA - to mining local outliers in high-dimensional data sets. To improve the efficiency of outlier detection, LOMA prunes irrelevance attributes and objects in the data set by analyzing attribute relevance with a sparse factor threshold. Such a pruning technique substantially reduce the size of data sets. The core of LOMA is searching sparse subspace, which implements the particle swarm optimization method in reduced data sets. In the process of searching sparse subspace, we introduce the sparse coefficient threshold to represent sparse degrees of data objects in a subspace, where the data objects are considered as local outliers. The attribute relevance analysis provides a guidance for experts and users to identify useless attributes for detecting outliers. In addition, our sparse-subspace-based outlier algorithm is a novel technique for local-outlier detection in a wide variety of applications. Experimental results driven by both synthetic and UCI data sets validate the effectiveness and accuracy of our LOMA. In particular, LOMA achieves high mining efficiency and accuracy when the sparse factor threshold is set to a small value. (C) 2017 Elsevier Ltd. 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