Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy
Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct clus...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Lin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Umfang: |
23 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:473 ; year:2022 ; day:7 ; month:02 ; pages:159-181 ; extent:23 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2021.12.019 |
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ELV056364261 |
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| 245 | 1 | 0 | |a Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy |
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| 520 | |a Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. | ||
| 520 | |a Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. | ||
| 650 | 7 | |a Local density |2 Elsevier | |
| 650 | 7 | |a Allocation strategy |2 Elsevier | |
| 650 | 7 | |a Cluster center |2 Elsevier | |
| 650 | 7 | |a Density peaks clustering |2 Elsevier | |
| 700 | 1 | |a Qin, Xiaoying |4 oth | |
| 700 | 1 | |a Ding, Weiping |4 oth | |
| 700 | 1 | |a Xu, Jiucheng |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Yang ELSEVIER |t The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |d 2018 |d an international journal |g Amsterdam |w (DE-627)ELV002603926 |
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10.1016/j.neucom.2021.12.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001629.pica (DE-627)ELV056364261 (ELSEVIER)S0925-2312(21)01853-1 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Lin verfasserin aut Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy 2022transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Local density Elsevier Allocation strategy Elsevier Cluster center Elsevier Density peaks clustering Elsevier Qin, Xiaoying oth Ding, Weiping oth Xu, Jiucheng oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:473 year:2022 day:7 month:02 pages:159-181 extent:23 https://doi.org/10.1016/j.neucom.2021.12.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 473 2022 7 0207 159-181 23 |
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10.1016/j.neucom.2021.12.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001629.pica (DE-627)ELV056364261 (ELSEVIER)S0925-2312(21)01853-1 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Lin verfasserin aut Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy 2022transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Local density Elsevier Allocation strategy Elsevier Cluster center Elsevier Density peaks clustering Elsevier Qin, Xiaoying oth Ding, Weiping oth Xu, Jiucheng oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:473 year:2022 day:7 month:02 pages:159-181 extent:23 https://doi.org/10.1016/j.neucom.2021.12.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 473 2022 7 0207 159-181 23 |
| allfields_unstemmed |
10.1016/j.neucom.2021.12.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001629.pica (DE-627)ELV056364261 (ELSEVIER)S0925-2312(21)01853-1 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Lin verfasserin aut Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy 2022transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Local density Elsevier Allocation strategy Elsevier Cluster center Elsevier Density peaks clustering Elsevier Qin, Xiaoying oth Ding, Weiping oth Xu, Jiucheng oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:473 year:2022 day:7 month:02 pages:159-181 extent:23 https://doi.org/10.1016/j.neucom.2021.12.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 473 2022 7 0207 159-181 23 |
| allfieldsGer |
10.1016/j.neucom.2021.12.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001629.pica (DE-627)ELV056364261 (ELSEVIER)S0925-2312(21)01853-1 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Lin verfasserin aut Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy 2022transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Local density Elsevier Allocation strategy Elsevier Cluster center Elsevier Density peaks clustering Elsevier Qin, Xiaoying oth Ding, Weiping oth Xu, Jiucheng oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:473 year:2022 day:7 month:02 pages:159-181 extent:23 https://doi.org/10.1016/j.neucom.2021.12.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 473 2022 7 0207 159-181 23 |
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10.1016/j.neucom.2021.12.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001629.pica (DE-627)ELV056364261 (ELSEVIER)S0925-2312(21)01853-1 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Lin verfasserin aut Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy 2022transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. Local density Elsevier Allocation strategy Elsevier Cluster center Elsevier Density peaks clustering Elsevier Qin, Xiaoying oth Ding, Weiping oth Xu, Jiucheng oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:473 year:2022 day:7 month:02 pages:159-181 extent:23 https://doi.org/10.1016/j.neucom.2021.12.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 473 2022 7 0207 159-181 23 |
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Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. |
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Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. |
| abstract_unstemmed |
Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. |
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