Outlier detection algorithm based on fast density peak clustering outlier factor
For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estim...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-10-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022193/ |
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| _version_ | 1841539980145459200 |
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| author | Zhongping ZHANG Sen LI Weixiong LIU Shuxia LIU |
| author_facet | Zhongping ZHANG Sen LI Weixiong LIU Shuxia LIU |
| author_sort | Zhongping ZHANG |
| collection | DOAJ |
| description | For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified. |
| format | Article |
| id | doaj-art-844f81b3ca594e60a000df895521831a |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-844f81b3ca594e60a000df895521831a2025-01-14T06:30:07ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-10-014318619559396441Outlier detection algorithm based on fast density peak clustering outlier factorZhongping ZHANGSen LIWeixiong LIUShuxia LIUFor the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022193/data miningdensity peak clusteringoutlierk nearest neighborcentripetal relative distance |
| spellingShingle | Zhongping ZHANG Sen LI Weixiong LIU Shuxia LIU Outlier detection algorithm based on fast density peak clustering outlier factor Tongxin xuebao data mining density peak clustering outlier k nearest neighbor centripetal relative distance |
| title | Outlier detection algorithm based on fast density peak clustering outlier factor |
| title_full | Outlier detection algorithm based on fast density peak clustering outlier factor |
| title_fullStr | Outlier detection algorithm based on fast density peak clustering outlier factor |
| title_full_unstemmed | Outlier detection algorithm based on fast density peak clustering outlier factor |
| title_short | Outlier detection algorithm based on fast density peak clustering outlier factor |
| title_sort | outlier detection algorithm based on fast density peak clustering outlier factor |
| topic | data mining density peak clustering outlier k nearest neighbor centripetal relative distance |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022193/ |
| work_keys_str_mv | AT zhongpingzhang outlierdetectionalgorithmbasedonfastdensitypeakclusteringoutlierfactor AT senli outlierdetectionalgorithmbasedonfastdensitypeakclusteringoutlierfactor AT weixiongliu outlierdetectionalgorithmbasedonfastdensitypeakclusteringoutlierfactor AT shuxialiu outlierdetectionalgorithmbasedonfastdensitypeakclusteringoutlierfactor |