An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm
Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra time overhead and makes the algorithm execution...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8735734/ |
| _version_ | 1819276597077737472 |
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| author | Ping Yang Dan Wang Zhuojun Wei Xiaolin Du Tong Li |
| author_facet | Ping Yang Dan Wang Zhuojun Wei Xiaolin Du Tong Li |
| author_sort | Ping Yang |
| collection | DOAJ |
| description | Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra time overhead and makes the algorithm execution inefficient. In addition, if the K-distance neighborhood of an outlier point P contains some outliers that are incorrectly judged by the algorithm as normal points, then P may be misidentified as normal point. To solve the above problems, this paper proposes a Neighbor Entropy Local Outlier Factor (NELOF) outlier detecting algorithm. Firstly, we improve the Self-Organizing Feature Map (SOFM) algorithm and use the optimized SOFM clustering algorithm to cluster the dataset. Therefore, the calculation of each data point's local outlier factor only needs to be performed inside the small cluster. Secondly, this paper replaces the K-distance neighborhood with relative K-distance neighborhood and utilizes the entropy of relative K neighborhood to redefine the local outlier factor, which improves the accuracy of outlier detection. Experiments results confirm that our optimized SOFM algorithm can avoid the random selection of neurons, and improve clustering effect of traditional SOFM algorithm. In addition, the proposed NELOF algorithm outperforms LOF algorithm in both accuracy and execution time of outlier detection. |
| first_indexed | 2024-12-23T23:42:45Z |
| format | Article |
| id | doaj.art-255b25808fb34fb9be2c0df158d33047 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:42:45Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-255b25808fb34fb9be2c0df158d330472022-12-21T17:25:36ZengIEEEIEEE Access2169-35362019-01-01711591411592510.1109/ACCESS.2019.29220048735734An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering AlgorithmPing Yang0https://orcid.org/0000-0002-7091-2289Dan Wang1Zhuojun Wei2Xiaolin Du3Tong Li4Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaHuawei Software Technology Co. Ltd., Nanjing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaLocal Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra time overhead and makes the algorithm execution inefficient. In addition, if the K-distance neighborhood of an outlier point P contains some outliers that are incorrectly judged by the algorithm as normal points, then P may be misidentified as normal point. To solve the above problems, this paper proposes a Neighbor Entropy Local Outlier Factor (NELOF) outlier detecting algorithm. Firstly, we improve the Self-Organizing Feature Map (SOFM) algorithm and use the optimized SOFM clustering algorithm to cluster the dataset. Therefore, the calculation of each data point's local outlier factor only needs to be performed inside the small cluster. Secondly, this paper replaces the K-distance neighborhood with relative K-distance neighborhood and utilizes the entropy of relative K neighborhood to redefine the local outlier factor, which improves the accuracy of outlier detection. Experiments results confirm that our optimized SOFM algorithm can avoid the random selection of neurons, and improve clustering effect of traditional SOFM algorithm. In addition, the proposed NELOF algorithm outperforms LOF algorithm in both accuracy and execution time of outlier detection.https://ieeexplore.ieee.org/document/8735734/Canopyclusteroutlier detectionLOFSOFM |
| spellingShingle | Ping Yang Dan Wang Zhuojun Wei Xiaolin Du Tong Li An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm IEEE Access Canopy cluster outlier detection LOF SOFM |
| title | An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm |
| title_full | An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm |
| title_fullStr | An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm |
| title_full_unstemmed | An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm |
| title_short | An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm |
| title_sort | outlier detection approach based on improved self organizing feature map clustering algorithm |
| topic | Canopy cluster outlier detection LOF SOFM |
| url | https://ieeexplore.ieee.org/document/8735734/ |
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