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Astronomy and Big Data : A Data Clustering Approach to Identifying Uncertain Galaxy Morphology.

Author: Kieran Jay Edwards; Mohamed Medhat Gaber
Publisher: Dordrecht : Springer, 2014.
Series: Studies in Big Data; EBL-Schweitzer
Edition/Format:   eBook : English : Online-AusgView all editions and formats
Summary:
Preface; Acknowledgements; Contents; Introduction; 1.1 Background; 1.2 Aims and Objectives; 1.3 Book Organisation; Astronomy, Galaxies and Stars: An Overview; 2.1 Why Astronomy?; 2.2 Galaxies and Stars; 2.2.1 Galaxy Morphology; 2.3 The Big Bang Theory; 2.4 Summary; Astronomical Data Mining; 3.1 Data Mining: Definition; 3.1.1 Applications and Challenges; 3.2 Galaxy Zoo: Citizen Science; 3.3 Galaxy Zoo/SDSS Data; 3.4
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Genre/Form: Electronic books
Material Type: Internet resource
Document Type: Internet Resource
All Authors / Contributors: Kieran Jay Edwards; Mohamed Medhat Gaber
ISBN: 9783319065991 3319065998
OCLC Number: 881303127
Notes: Description based upon print version of record.
Description: Online-Ressource (1 online resource (112 p.))
Series Title: Studies in Big Data; EBL-Schweitzer

Abstract:

Preface; Acknowledgements; Contents; Introduction; 1.1 Background; 1.2 Aims and Objectives; 1.3 Book Organisation; Astronomy, Galaxies and Stars: An Overview; 2.1 Why Astronomy?; 2.2 Galaxies and Stars; 2.2.1 Galaxy Morphology; 2.3 The Big Bang Theory; 2.4 Summary; Astronomical Data Mining; 3.1 Data Mining: Definition; 3.1.1 Applications and Challenges; 3.2 Galaxy Zoo: Citizen Science; 3.3 Galaxy Zoo/SDSS Data; 3.4 Data Pre-processing and Attribute Selection; 3.5 Applied Techniques/Tasks; 3.6 Summary and Discussion; Adopted Data Mining Methods.

4.1 CRoss-Industry Standard Process for Data Mining (CRISP-DM)4.2 K-Means; 4.3 Support Vector Machines; 4.3.1 Sequential Minimal Optimisation; 4.4 Random Forests; 4.5 Incremental Feature Selection (IFS) Algorithm; 4.6 Pre- and Post-processing; 4.6.1 Pre-processing; 4.6.2 Post-processing; 4.7 Summary; Research Methodology; 5.1 Galaxy Zoo Table 2; 5.2 Data Mining the Galaxy Zoo Mergers; 5.3 Extensive SDSS Data Analysis; 5.3.1 Isolating and Re-Clustering Galaxies Labelled as; Development of Data Mining Models; 6.1 Waikato Environment for Knowledge Analysis (WEKA); 6.1.1 WEKA Implementations.

6.1.2 Initial Experimentation on Galaxy Zoo Table 2 Data Set6.1.3 Experiments with; 6.2 R Language and RStudio; 6.2.1 RStudio Implementation; 6.3 MySQL Database Queries; 6.4 Development of Knowledge-Flow Models; 6.5 Summary; Experimentation Results; 7.1 Galaxy Zoo Table 2 Clustering Results; 7.2 Clustering Results of Lowest DBI Attributes; 7.3 Extensive SDSS Analysis Results; 7.4 Results of; 7.5 Results of Further Experimentation; 7.6 Summary; Conclusion and FutureWork; 8.1 Conclusion; 8.1.1 Experimental Remarks; 8.2 Future Work and Big Data; 8.2.1 Analysis of Data Storage Representation.

8.2.2 Output Storage Representation8.2.3 Data Mining and Storage Workflow; 8.2.4 Development and Adoption of Data Mining Techniques; 8.2.5 Providing Astronomers with Insights; 8.3 FinalWords; References; Index.

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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