Privacy preserving Data Mining by Inverse Frequent Item Set Mining: Data Mining Approach - Softcover
Inhaltsangabe
An emerging research topic in data mining, known as privacy-preserving data mining (PPDM) is discussed in this book. It is an application of data mining research in response to privacy security in data mining. It is called a privacy-enhanced or privacy-sensitive data mining. Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. This book provides a panoramic overview on new perspective and systematic interpretation of a list published literature's via their meticulous organization in subcategories. The PPDM techniques can be classified based on 3 Characteristics: data distribution, purposes of hiding, Data Mining algorithms.Data mining is very important tool used by organizations for providing better service, achieving greater profit, and better decision-making. But privacy and security concerns may create barrier in data mining task. These barriers can be removed by applying PPDM techniques and by ensuring security in data mining task. Many techniques have been proposed for PPDM, with each technique having some advantages over another in its own terms.
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Assistenzprofessor an der Sanjay Bhokare Group of Institute von ATS, Miraj.Maximieren Sie die Leistung durch die Umsetzung geeigneter Standards durch die Analyse von Details, um das Verständnis für neue Best Practices und Rechtsvorschriften zu gewinnen.
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Privacy preserving Data Mining by Inverse Frequent Item Set Mining: Data Mining Approach
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Privacy preserving Data Mining by Inverse Frequent Item Set Mining: Data Mining Approach
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Privacy preserving Data Mining by Inverse Frequent Item Set Mining
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -An emerging research topic in data mining, known as privacy-preserving data mining (PPDM) is discussed in this book. It is an application of data mining research in response to privacy security in data mining. It is called a privacy-enhanced or privacy-sensitive data mining. Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. This book provides a panoramic overview on new perspective and systematic interpretation of a list published literature's via their meticulous organization in subcategories. The PPDM techniques can be classified based on 3 Characteristics: data distribution, purposes of hiding, Data Mining algorithms.Data mining is very important tool used by organizations for providing better service, achieving greater profit, and better decision-making. But privacy and security concerns may create barrier in data mining task. These barriers can be removed by applying PPDM techniques and by ensuring security in data mining task. Many techniques have been proposed for PPDM, with each technique having some advantages over another in its own terms. 64 pp. Englisch. Bestandsnummer des Verkäufers 9786205488102
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Privacy preserving Data Mining by Inverse Frequent Item Set Mining: Data Mining Approach
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LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205488108
ISBN 13: 9786205488102
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM) is discussed in this book. It is an application of data mining research in response to privacy security in data mining. It is called a privacy-enhanced or privacy-sens. Bestandsnummer des Verkäufers 699354112
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Privacy preserving Data Mining by Inverse Frequent Item Set Mining
Verlag:
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ISBN 10: 6205488108
ISBN 13: 9786205488102
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -An emerging research topic in data mining, known as privacy-preserving data mining (PPDM) is discussed in this book. It is an application of data mining research in response to privacy security in data mining. It is called a privacy-enhanced or privacy-sensitive data mining. Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. This book provides a panoramic overview on new perspective and systematic interpretation of a list published literature's via their meticulous organization in subcategories. The PPDM techniques can be classified based on 3 Characteristics: data distribution, purposes of hiding, Data Mining algorithms.Data mining is very important tool used by organizations for providing better service, achieving greater profit, and better decision-making. But privacy and security concerns may create barrier in data mining task. These barriers can be removed by applying PPDM techniques and by ensuring security in data mining task. Many techniques have been proposed for PPDM, with each technique having some advantages over another in its own terms.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. Bestandsnummer des Verkäufers 9786205488102
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Privacy preserving Data Mining by Inverse Frequent Item Set Mining : Data Mining Approach
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LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205488108
ISBN 13: 9786205488102
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - An emerging research topic in data mining, known as privacy-preserving data mining (PPDM) is discussed in this book. It is an application of data mining research in response to privacy security in data mining. It is called a privacy-enhanced or privacy-sensitive data mining. Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. This book provides a panoramic overview on new perspective and systematic interpretation of a list published literature's via their meticulous organization in subcategories. The PPDM techniques can be classified based on 3 Characteristics: data distribution, purposes of hiding, Data Mining algorithms.Data mining is very important tool used by organizations for providing better service, achieving greater profit, and better decision-making. But privacy and security concerns may create barrier in data mining task. These barriers can be removed by applying PPDM techniques and by ensuring security in data mining task. Many techniques have been proposed for PPDM, with each technique having some advantages over another in its own terms. Bestandsnummer des Verkäufers 9786205488102
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Privacy preserving Data Mining by Inverse Frequent Item Set Mining | Data Mining Approach
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ISBN 13: 9786205488102
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Taschenbuch. Zustand: Neu. Privacy preserving Data Mining by Inverse Frequent Item Set Mining | Data Mining Approach | Ashwini J. Sawakhande | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205488102 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 123946328
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