Casebook on Data Protection - Couverture souple

Babalola, Olumide

9786202553551: Casebook on Data Protection

Couverture souple

ISBN 10 : 6202553553 ISBN 13 : 9786202553551

Editeur : Noetico Repertum Inc. Lagos Nigeria, 2020

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Casebook on Data Protection is a collection of 144 decisions of the European Court of Human Rights (ECTHR) and the Court of Justice of the European Union (CJEU) on data protection and privacy. The facts and decisions are summarised and featured to the extent that they relate to the field of data protection and/or privacy. The book is divided into 14 chapters to wit: Introduction; Definitions; Relationship with other rights; Principles of Data Protection; Exceptions and Derogation; Employment Data; Sensitive Data; Transfer of Data to a Foreign Country; Liability of Data Controllers; Data Subject’s Rights; Data Breach; Remedies; Data Property Rights and Supervisory Authority. Its appendices contain Nigeria Data Protection Regulation and its draft implementation framework.

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�diteur: Noetico Repertum Inc. Lagos Nigeria
Date d'�dition: 2020
Langue: anglais
ISBN 10: 6202553553
ISBN 13: 9786202553551
Reliure: Broch�
Nombre de pages: 711
Coordonn�es du fabricant: Manufactured by Amazon on behalf of the author
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Casebook on Data Protection

Navneet Kaur

Edit� par LAP Lambert Academic Publishing, 2021

ISBN 10 : 6202553553 ISBN 13 : 9786202553551

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Casebook on Data Protection

Navneet Kaur

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Casebook on Data Protection

Babalola, Olumide

Edit� par Noetico Repertum Inc. Lagos Nigeria, 2020

ISBN 10 : 6202553553 ISBN 13 : 9786202553551

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Casebook on Data Protection: Outlier Detection Using A New Hybrid Approach On Mixed Dataset

Olumide Babalola

Edit� par Noetico Repertum Inc. Lagos Nigeria 2020-06-24, 2020

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Outlier Detection Using A New Hybrid Approach On Mixed Dataset

Navneet Kaur

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining is a process of extracting hidden and useful information from the data. Outlier detection is a fundamental part of data mining and has huge attention from the research community recently. An outlier is data object that deviates from other observations. Detecting outliers has important applications in data cleaning as well as in the mining of abnormal points for fraud detection, stock market analysis, intrusion detection, marketing, network sensors. Most of the existing research efforts focus on numerical datasets which are not directly applicable on categorical dataset where there is little sense in ordering the data and calculating distances among data points. Furthermore, a number of the current outlier detection methods require quadratic time with respect to the dataset size and usually need multiple scans of the data; these features are undesirable when the datasets are large. This thesis focuses and evaluates, experimentally, an outlier detection approach that is geared towards categorical sets. In addition, this is a simple, scalable and efficient outlier detection algorithm that has the advantage of discovering outliers in categorical or numerical datasets by per 64 pp. Englisch. N� de r�f. du vendeur 9786202553551

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Casebook on Data Protection

Babalola, Olumide

Edit� par Noetico Repertum Inc. Lagos Nigeria, 2020

ISBN 10 : 6202553553 ISBN 13 : 9786202553551

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Casebook on Data Protection

Babalola, Olumide

Edit� par Noetico Repertum Inc. Lagos Nigeria, 2020

ISBN 10 : 6202553553 ISBN 13 : 9786202553551

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Outlier Detection Using A New Hybrid Approach On Mixed Dataset

Navneet Kaur

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kaur NavneetFirst of all, I am thankful to God for his blessings and showing me the right direction. With his mercy, it has been made possible for me to reach so far.I AM AN ASSISTANT PROFESSOR AT SHRI GURU TEG BAHADUR KHALSA COLLEGE. N� de r�f. du vendeur 456395579

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Outlier Detection Using A New Hybrid Approach On Mixed Dataset

Navneet Kaur

Edit� par LAP LAMBERT Academic Publishing Feb 2021, 2021

ISBN 10 : 6202553553 ISBN 13 : 9786202553551

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data mining is a process of extracting hidden and useful information from the data. Outlier detection is a fundamental part of data mining and has huge attention from the research community recently. An outlier is data object that deviates from other observations. Detecting outliers has important applications in data cleaning as well as in the mining of abnormal points for fraud detection, stock market analysis, intrusion detection, marketing, network sensors. Most of the existing research efforts focus on numerical datasets which are not directly applicable on categorical dataset where there is little sense in ordering the data and calculating distances among data points. Furthermore, a number of the current outlier detection methods require quadratic time with respect to the dataset size and usually need multiple scans of the data; these features are undesirable when the datasets are large. This thesis focuses and evaluates, experimentally, an outlier detection approach that is geared towards categorical sets. In addition, this is a simple, scalable and efficient outlier detection algorithm that has the advantage of discovering outliers in categorical or numerical datasets by perVDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 64 pp. Englisch. N� de r�f. du vendeur 9786202553551

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Outlier Detection Using A New Hybrid Approach On Mixed Dataset : Outlier Detection Using A New Hybrid Approach On Mixed Dataset

Navneet Kaur

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ISBN 10 : 6202553553 ISBN 13 : 9786202553551

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data mining is a process of extracting hidden and useful information from the data. Outlier detection is a fundamental part of data mining and has huge attention from the research community recently. An outlier is data object that deviates from other observations. Detecting outliers has important applications in data cleaning as well as in the mining of abnormal points for fraud detection, stock market analysis, intrusion detection, marketing, network sensors. Most of the existing research efforts focus on numerical datasets which are not directly applicable on categorical dataset where there is little sense in ordering the data and calculating distances among data points. Furthermore, a number of the current outlier detection methods require quadratic time with respect to the dataset size and usually need multiple scans of the data; these features are undesirable when the datasets are large. This thesis focuses and evaluates, experimentally, an outlier detection approach that is geared towards categorical sets. In addition, this is a simple, scalable and efficient outlier detection algorithm that has the advantage of discovering outliers in categorical or numerical datasets by per. N� de r�f. du vendeur 9786202553551

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