Studies in Big Data

Astronomy and Big Data

A Data Clustering Approach to Identifying Uncertain Galaxy Morphology

Authors: Edwards, Kieran, Gaber, Mohamed Medhat

  • Presents recent applications of Big Data research to Astronomy
  • Demonstrates the application of Big data to the Galaxy Zoo project, where a large collection of galaxy images are annotated by citizen scientists
  • Presents a Data Clustering Approach to Identifying Uncertain Galaxy Morphology
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  • ISBN 978-3-319-06599-1
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Hardcover $129.00
price for USA
  • ISBN 978-3-319-06598-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: October 21, 2016
  • ISBN 978-3-319-38328-6
  • Free shipping for individuals worldwide
Rent the ebook  
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About this book

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.

Table of contents (8 chapters)

  • Introduction

    Edwards, Kieran Jay (et al.)

    Pages 1-3

  • Astronomy, Galaxies and Stars: An Overview

    Edwards, Kieran Jay (et al.)

    Pages 5-14

  • Astronomical Data Mining

    Edwards, Kieran Jay (et al.)

    Pages 15-30

  • Adopted Data Mining Methods

    Edwards, Kieran Jay (et al.)

    Pages 31-42

  • Research Methodology

    Edwards, Kieran Jay (et al.)

    Pages 43-48

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-3-319-06599-1
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-319-06598-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: October 21, 2016
  • ISBN 978-3-319-38328-6
  • Free shipping for individuals worldwide
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Astronomy and Big Data
Book Subtitle
A Data Clustering Approach to Identifying Uncertain Galaxy Morphology
Authors
Series Title
Studies in Big Data
Series Volume
6
Copyright
2014
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-06599-1
DOI
10.1007/978-3-319-06599-1
Hardcover ISBN
978-3-319-06598-4
Softcover ISBN
978-3-319-38328-6
Series ISSN
2197-6503
Edition Number
1
Number of Pages
XII, 105
Number of Illustrations and Tables
30 b/w illustrations, 24 illustrations in colour
Topics