Abstract
In order to improve the efficiency of the method by clustering outlier detection, outlier mining algorithm based on approximate outlier factor (OMAAOF) algorithm based on approximate outlier factor is proposed in this paper. The algorithm first presents the definition of the approximate distance and outlier approximate coefficient, then provides an heuristic pruning strategies to reduce the suspect candidate sets to decrease the computational complexity. Experiments have been carried out with public datasets iris, labour and segment-test. The experimental results show that the performance of OMAAOF is effective.
| Original language | English |
|---|---|
| Pages (from-to) | 243-256 |
| Number of pages | 14 |
| Journal | International Journal of Autonomous and Adaptive Communications Systems |
| Volume | 8 |
| Issue number | 2/3 |
| DOIs | |
| Publication status | Published - May 2015 |
Keywords
- outlier detection
- outlying degree
- pruning strategy
- outlier mining
- clustering
- approximate distance
- outlier approximate coefficient
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Cite this
Liao, Z., Liu, L., Fan, X., Xie, Y., Liao, Z., & Zhang, Y. (2015). An outlier mining algorithm based on approximate outlier factor. International Journal of Autonomous and Adaptive Communications Systems, 8(2/3), 243-256. https://doi.org/10.1504/IJAACS.2015.069567