Hidden Bibliographic Details
| Other authors / contributors: | Gaber, Mohamed Medhat.
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| ISBN: | 9783319065991
3319065998
1306702550
9781306702553
331906598X
9783319065984
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| Notes: | Includes bibliographical references and index.
Print version record.
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| Summary: | With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as "Uncertain". This book reports on how to use data mining, more specifically clustering, to identify gal.
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| Other form: | Print version: Edwards, Kieran Jay. Astronomy and Big Data : A Data Clustering Approach to Identifying Uncertain Galaxy Morphology. Dordrecht : Springer, ©2014 9783319065984
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| Standard no.: | 10.1007/978-3-319-06599-1
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