Astronomy and Big Data: A Data Clustering Approach to Identifying Uncertain Galaxy Morphology - Couverture rigide
Livre 6 sur 108: Studies in Big Data
Synopsis
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 galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.
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Astronomy and Big Data. A Data Clustering Approach to Identifying Uncertain Galaxy Morphology.
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ISBN 13 : 9783319065984
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Astronomy and Big Data : A Data Clustering Approach to Identifying Uncertain Galaxy Morphology
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Astronomy and Big Data : A Data Clustering Approach to Identifying Uncertain Galaxy Morphology
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Springer International Publishing, 2014
ISBN 10 : 331906598X
ISBN 13 : 9783319065984
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Astronomy and Big Data
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Springer International Publishing Apr 2014, 2014
ISBN 10 : 331906598X
ISBN 13 : 9783319065984
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants. 120 pp. Englisch. N° de réf. du vendeur 9783319065984
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Astronomy and Big Data
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Springer International Publishing, 2014
ISBN 10 : 331906598X
ISBN 13 : 9783319065984
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent applications of Big Data research to AstronomyDemonstrates the application of Big data to the Galaxy Zoo project, where a large collection of galaxy images are annotated by citizen scientistsPresents a Data Clustering Approa. N° de réf. du vendeur 4497621
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Astronomy and Big Data: A Data Clustering Approach to Identifying Uncertain Galaxy Morphology
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Springer-Verlag New York Inc, 2014
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ISBN 13 : 9783319065984
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Hardcover. Etat : Brand New. 2014 edition. 103 pages. 9.25x6.25x0.50 inches. In Stock. N° de réf. du vendeur x-331906598X
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Astronomy and Big Data
ISBN 10 : 331906598X
ISBN 13 : 9783319065984
Neuf
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Buch. Etat : Neu. Neuware -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 galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch. N° de réf. du vendeur 9783319065984
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