LEADER 04479nam 22007575 450 001 9910741140603321 005 20200702035607.0 010 $a3-319-06599-8 024 7 $a10.1007/978-3-319-06599-1 035 $a(CKB)2550000001280357 035 $a(EBL)1697955 035 $a(OCoLC)881165973 035 $a(SSID)ssj0001204806 035 $a(PQKBManifestationID)11662764 035 $a(PQKBTitleCode)TC0001204806 035 $a(PQKBWorkID)11180376 035 $a(PQKB)10345478 035 $a(MiAaPQ)EBC1697955 035 $a(DE-He213)978-3-319-06599-1 035 $a(PPN)178320080 035 $a(EXLCZ)992550000001280357 100 $a20140412d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAstronomy and Big Data $eA Data Clustering Approach to Identifying Uncertain Galaxy Morphology /$fby Kieran Jay Edwards, Mohamed Medhat Gaber 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (112 p.) 225 1 $aStudies in Big Data,$x2197-6503 ;$v6 300 $aDescription based upon print version of record. 311 $a3-319-06598-X 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- 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. 330 $aWith the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as ?Uncertain?. This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants. 410 0$aStudies in Big Data,$x2197-6503 ;$v6 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aObservations, Astronomical 606 $aAstronomy?Observations 606 $aData mining 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aAstronomy, Observations and Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/P22014 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aObservations, Astronomical. 615 0$aAstronomy?Observations. 615 0$aData mining. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aAstronomy, Observations and Techniques. 615 24$aData Mining and Knowledge Discovery. 676 $a520.222 700 $aEdwards$b Kieran Jay$4aut$4http://id.loc.gov/vocabulary/relators/aut$01424427 702 $aGaber$b Mohamed Medhat$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910741140603321 996 $aAstronomy and Big Data$93553607 997 $aUNINA