Difference between revisions of "File:Fig2 Murtagh BigDataCogComp2018 2-2.jpg"

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==Summary==
{{Information
|Description='''Figure 2.''' Hierarchical clustering, and derived 3-class partition, of the 21 software languages or environments at issue here, based on the full dimensionality, Euclidean-metric endowed, factor space. See Section 3. The agglomerative criterion is the Ward minimum variance method.
|Source={{cite journal |title=The development of data science: Implications for education, employment, research, and the data revolution for sustainable development |journal=Big Data and Cognitive Computing |author=Murtagh, F.; Devlin, K. |volume=2 |issue=2 |page=14 |year=2018 |doi=10.3390/bdcc2020014}}
|Author=Murtagh, F.; Devlin, K.
|Date=2018
|Permission=[http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International]
}}
== Licensing ==
== Licensing ==
{{cc-by-4.0}}
{{cc-by-4.0}}

Latest revision as of 20:31, 17 July 2018

Summary

Description

Figure 2. Hierarchical clustering, and derived 3-class partition, of the 21 software languages or environments at issue here, based on the full dimensionality, Euclidean-metric endowed, factor space. See Section 3. The agglomerative criterion is the Ward minimum variance method.

Source

Murtagh, F.; Devlin, K. (2018). "The development of data science: Implications for education, employment, research, and the data revolution for sustainable development". Big Data and Cognitive Computing 2 (2): 14. doi:10.3390/bdcc2020014. 

Date

2018

Author

Murtagh, F.; Devlin, K.

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Creative Commons Attribution 4.0 International

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