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Engineering - Computational Intelligence and Complexity | Astronomy and Big Data - A Data Clustering Approach to Identifying Uncertain Galaxy Morphology

Astronomy and Big Data

A Data Clustering Approach to Identifying Uncertain Galaxy Morphology

Series: Studies in Big Data, Vol. 6

Edwards, Kieran, Gaber, Mohamed Medhat

2014, XII, 105 p. 54 illus., 24 illus. in color.

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  • 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

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.

Content Level » Research

Keywords » Astronomy - Big Data - Citizen Science - Data Clustering - Galaxy Morphology - Galaxy Zoo Project

Related subjects » Artificial Intelligence - Astronomy, Observations and Techniques - Computational Intelligence and Complexity - Database Management & Information Retrieval

Table of contents 

Introduction.- Astronomy, Galaxies and Stars: An Overview.- Astronomical Data Mining.- Adopted Data Mining Methods.- Research Methodology.- Development of Data Mining Models.- Experimentation Results.- Conclusion and Future Work.

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