Outlier detection algorithm based on k-nearest neighbors-local outlier factor
The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the limitations of the existing outlier detection...
Saved in:
| Published in: | Journal of algorithms & computational technology Vol. 16 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London, England
SAGE Publications
01.03.2022
Sage Publications Ltd SAGE Publishing |
| Subjects: | |
| ISSN: | 1748-3018, 1748-3026 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the limitations of the existing outlier detection methods, we propose a novel outlier detection algorithm which is named as kNN-LOF. First, the k-nearest neighbors algorithm is applied to divide different areas for outlier attributes, which is more suitable for outlier detection in different density distributions. Secondly, a hierarchical adjacency order is proposed to hierarchize the neighborhood range according to the link distance. The average sequence distance is calculated from the data objects in the hierarchy, and the reachable distance of an object is redefined to introduce a new local outlier factor. Experimental results show that the proposed algorithm has good performance in improving the accuracy of outlier detection. |
|---|---|
| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1748-3018 1748-3026 |
| DOI: | 10.1177/17483026221078111 |