Abstract
| 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 |
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Keywords
- outlier detection
- outlying degree
- pruning strategy
- outlier mining
- clustering
- approximate distance
- outlier approximate coefficient
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An outlier mining algorithm based on approximate outlier factor. / Liao, Zhifang; Liu, Limin ; Fan, Xiaoping ; Xie, Yueshan; Liao, Zhining ; Zhang, Yan.
In: International Journal of Autonomous and Adaptive Communications Systems, Vol. 8, No. 2/3, 05.2015, p. 243-256.Research output: Contribution to journal › Article
TY - JOUR
T1 - An outlier mining algorithm based on approximate outlier factor
AU - Liao, Zhifang
AU - Liu, Limin
AU - Fan, Xiaoping
AU - Xie, Yueshan
AU - Liao, Zhining
AU - Zhang, Yan
PY - 2015/5
Y1 - 2015/5
N2 - 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.
AB - 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.
KW - outlier detection
KW - outlying degree
KW - pruning strategy
KW - outlier mining
KW - clustering
KW - approximate distance
KW - outlier approximate coefficient
U2 - 10.1504/IJAACS.2015.069567
DO - 10.1504/IJAACS.2015.069567
M3 - Article
VL - 8
SP - 243
EP - 256
JO - International Journal of Autonomous and Adaptive Communications Systems
JF - International Journal of Autonomous and Adaptive Communications Systems
SN - 1754-8632
IS - 2/3
ER -