000 04714nam a22006375i 4500
999 _c12059
_d12059
001 978-3-319-54024-5
003 DE-He213
005 20211130094359.0
008 170509s2017 gw | s |||| 0|eng d
020 _a9783319540245
040 _cAIKTC-KRRC
041 _aENG
072 7 _aUNF
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
245 1 0 _aTransparent Data Mining for Big and Small Data
_h[electronic resource] /
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXV, 215 p. 23 illus. in color.
_bCard Paper
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Big Data,
_x2197-6503 ;
_v32
520 _aThis book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches. As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.
650 0 _aComputer Engineering
_94622
653 _aMass media.
653 _aData Mining and Knowledge Discovery.
653 _aIT Law, Media Law, Intellectual Property.
653 _aAlgorithm Analysis and Problem Complexity.
700 1 _aCerquitelli, Tania.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aQuercia, Daniele.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aPasquale, Frank.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319540238
776 0 8 _iPrinted edition:
_z9783319540252
776 0 8 _iPrinted edition:
_z9783319852997
830 0 _aStudies in Big Data,
_x2197-6503 ;
_v32
856 4 0 _uhttps://doi.org/10.1007/978-3-319-54024-5
_zClick here to access eBook in Springer Nature platform. (Within Campus only.)
942 _cEBOOKS
_2ddc