Identificacion Principal del registro INGC-EBK-000529

Control Number Identifier AR-LpUFI

LAST MODIFICATION DATE 20160908091655

CONTROL FIELD cr nn 008mamaa

CONTROL FIELD 140412s2014 gw | s |||| 0|eng d

a International Standard Book Number 9783319065991

a Standard number or code 10.1007/978-3-319-06599-1

a Personal name Edwards, Kieran Jay.

a Title Astronomy and Big Data

h Medium [libro electrónico] : ;

b Remainder of title A Data Clustering Approach to Identifying Uncertain Galaxy Morphology /

c Statement of responsibility, etc by Kieran Jay Edwards, Mohamed Medhat Gaber.

a Place of publication, distribution, etc Cham :

b Name of publisher, distributor, etc Springer International Publishing :

b Name of publisher, distributor, etc Imprint: Springer,

c Date of publication, distribution, etc 2014.

a Extent xii, 105 p. :

b Other physical details il.

a Series statement Studies in Big Data,

x International Standard Serial Number 2197-6503 ;

v Volume number/sequential designation 6

a Formatted contents note 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.

a Summary, etc 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 â_oUncertainâ__. 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.

a Topical term or geographic name as entry element Engineering.

a Topical term or geographic name as entry element Data mining.

a Topical term or geographic name as entry element Observations, Astronomical.

a Topical term or geographic name as entry element Astronomy

x General subdivision Observations.

a Topical term or geographic name as entry element Computational intelligence.

a Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).

a Topical term or geographic name as entry element Knowledge Discovery.

a Personal name Gaber, Mohamed Medhat.

u Uniform Resource Identifier (R) http://dx.doi.org/10.1007/978-3-319-06599-1

c item type EBK

a descripción COM