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Transparent Data Mining for Big and Small Data : Engineering (R0) - Daniele Quercia

Transparent Data Mining for Big and Small Data

By: Daniele Quercia (Editor), Frank Pasquale (Editor), Tania Cerquitelli (Editor)

eText | 9 May 2017

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This 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.

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Non-FictionComputing & I.T.DatabasesData MiningLawJurisprudence & General IssuesComputer Programming & Software DevelopmentAlgorithms & Data StructuresEngineering & TechnologyTechnology in GeneralEngineering in GeneralGraphical & Digital Media Applications
3D Graphics & ModellingIndustry & Industrial StudiesMedia, Entertainment, Information & Communication IndustriesInformation Technology IndustriesLaws of Specific JurisdictionsEntertainment & Media LawBusiness & ManagementBusiness Mathematics & SystemsInternational LawMathematicsProbability & StatisticsReference, Information & Interdisciplinary SubjectsResearch & InformationInformation theoryCybernetics & Systems TheoryLibrary & Info SciencesLibrary & Information Services

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