Multi-view clustering with exemplars for scientific mapping
Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific jo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Meng, Xiangfeng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Akadémiai Kiadó, Budapest, Hungary 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Scientometrics - Springer Netherlands, 1978, 105(2015), 3 vom: 04. Sept., Seite 1527-1552 |
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| Übergeordnetes Werk: |
volume:105 ; year:2015 ; number:3 ; day:04 ; month:09 ; pages:1527-1552 |
| Links: |
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| DOI / URN: |
10.1007/s11192-015-1682-7 |
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OLC2033208753 |
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| 520 | |a Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. | ||
| 650 | 4 | |a Scientific mapping | |
| 650 | 4 | |a Affinity propagation | |
| 650 | 4 | |a Text mining | |
| 650 | 4 | |a Link analysis | |
| 650 | 4 | |a Multi-view clustering | |
| 700 | 1 | |a Liu, Xinhai |4 aut | |
| 700 | 1 | |a Tong, YunHai |4 aut | |
| 700 | 1 | |a Glänzel, Wolfgang |4 aut | |
| 700 | 1 | |a Tan, Shaohua |4 aut | |
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10.1007/s11192-015-1682-7 doi (DE-627)OLC2033208753 (DE-He213)s11192-015-1682-7-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Meng, Xiangfeng verfasserin aut Multi-view clustering with exemplars for scientific mapping 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2015 Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. Scientific mapping Affinity propagation Text mining Link analysis Multi-view clustering Liu, Xinhai aut Tong, YunHai aut Glänzel, Wolfgang aut Tan, Shaohua aut Enthalten in Scientometrics Springer Netherlands, 1978 105(2015), 3 vom: 04. Sept., Seite 1527-1552 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:105 year:2015 number:3 day:04 month:09 pages:1527-1552 https://doi.org/10.1007/s11192-015-1682-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 105 2015 3 04 09 1527-1552 |
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10.1007/s11192-015-1682-7 doi (DE-627)OLC2033208753 (DE-He213)s11192-015-1682-7-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Meng, Xiangfeng verfasserin aut Multi-view clustering with exemplars for scientific mapping 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2015 Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. Scientific mapping Affinity propagation Text mining Link analysis Multi-view clustering Liu, Xinhai aut Tong, YunHai aut Glänzel, Wolfgang aut Tan, Shaohua aut Enthalten in Scientometrics Springer Netherlands, 1978 105(2015), 3 vom: 04. Sept., Seite 1527-1552 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:105 year:2015 number:3 day:04 month:09 pages:1527-1552 https://doi.org/10.1007/s11192-015-1682-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 105 2015 3 04 09 1527-1552 |
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10.1007/s11192-015-1682-7 doi (DE-627)OLC2033208753 (DE-He213)s11192-015-1682-7-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Meng, Xiangfeng verfasserin aut Multi-view clustering with exemplars for scientific mapping 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2015 Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. Scientific mapping Affinity propagation Text mining Link analysis Multi-view clustering Liu, Xinhai aut Tong, YunHai aut Glänzel, Wolfgang aut Tan, Shaohua aut Enthalten in Scientometrics Springer Netherlands, 1978 105(2015), 3 vom: 04. Sept., Seite 1527-1552 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:105 year:2015 number:3 day:04 month:09 pages:1527-1552 https://doi.org/10.1007/s11192-015-1682-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 105 2015 3 04 09 1527-1552 |
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10.1007/s11192-015-1682-7 doi (DE-627)OLC2033208753 (DE-He213)s11192-015-1682-7-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Meng, Xiangfeng verfasserin aut Multi-view clustering with exemplars for scientific mapping 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2015 Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. Scientific mapping Affinity propagation Text mining Link analysis Multi-view clustering Liu, Xinhai aut Tong, YunHai aut Glänzel, Wolfgang aut Tan, Shaohua aut Enthalten in Scientometrics Springer Netherlands, 1978 105(2015), 3 vom: 04. Sept., Seite 1527-1552 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:105 year:2015 number:3 day:04 month:09 pages:1527-1552 https://doi.org/10.1007/s11192-015-1682-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 105 2015 3 04 09 1527-1552 |
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Multi-view clustering with exemplars for scientific mapping |
| abstract |
Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. © Akadémiai Kiadó, Budapest, Hungary 2015 |
| abstractGer |
Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. © Akadémiai Kiadó, Budapest, Hungary 2015 |
| abstract_unstemmed |
Abstract Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. © Akadémiai Kiadó, Budapest, Hungary 2015 |
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| title_short |
Multi-view clustering with exemplars for scientific mapping |
| url |
https://doi.org/10.1007/s11192-015-1682-7 |
| remote_bool |
false |
| author2 |
Liu, Xinhai Tong, YunHai Glänzel, Wolfgang Tan, Shaohua |
| author2Str |
Liu, Xinhai Tong, YunHai Glänzel, Wolfgang Tan, Shaohua |
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13005352X |
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| doi_str |
10.1007/s11192-015-1682-7 |
| up_date |
2024-07-03T16:08:23.188Z |
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7.3994465 |