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Anglický jazyk
Privacy preserving Data Mining by Inverse Frequent Item Set Mining
Autor: Ashwini J. Sawakhande
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... Viac o knihe
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O knihe
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.
- Vydavateľstvo: LAP LAMBERT Academic Publishing
- Rok vydania: 2022
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Anglický jazyk
- ISBN: 9786205488102
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