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Correlation based approach for hiding sensitive items in data mining
Editore:
LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6139838231
ISBN 13: 9786139838233
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main goal of data mining is to extract high level or hidden information from large databases. Along with the advantage of extracting useful pattern, it also poses threats of revealing user¿s sensitive information. We can hide sensitive information of the user by using privacy preservation data mining(PPDM). In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. As association rule is a key tool for finding such patterns, certain association rules can be categorized as sensitive if its disclosure risk is above some given specified threshold. Most privacy preserving data mining approaches use support and confidence. Author in this book proposed correlation based approach which uses measures other than support and confidence such as correlation among items in sensitive itemsets to hide the sensitive frequent itemsets. Columns in dataset having a specified correlation threshold value are considered for hiding process. This mechanism is called Pearson¿s correlation coefficient weighing mechanism which maintains the trade off between privacy and acuuracy. 168 pp. Englisch. Codice articolo 9786139838233
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Correlation based approach for hiding sensitive items in data mining
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139838231
ISBN 13: 9786139838233
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Brossura
Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sreenivasa Rao KunchamKuncham Sreenivasa Rao has done his B.Tech in Computer Science and Engineering (CSE) from JNT University, Hyderabad in the year 2005, M.Tech in CSE from JNT University Kakinada in the year 2009. He Obtained his. Codice articolo 504548460
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Correlation based approach for hiding sensitive items in data mining: A novel approach for Privacy Preserving Data Mining
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139838231
ISBN 13: 9786139838233
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Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. Codice articolo 26397279397
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Correlation based approach for hiding sensitive items in data mining: A novel approach for Privacy Preserving Data Mining
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139838231
ISBN 13: 9786139838233
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Da: Majestic Books, Hounslow, Regno Unito
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Condizione: New. Print on Demand. Codice articolo 400146298
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Correlation based approach for hiding sensitive items in data mining: A novel approach for Privacy Preserving Data Mining
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139838231
ISBN 13: 9786139838233
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Brossura
Da: Biblios, Frankfurt am main, HESSE, Germania
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Condizione: New. PRINT ON DEMAND. Codice articolo 18397279407
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Correlation based approach for hiding sensitive items in data mining | A novel approach for Privacy Preserving Data Mining
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139838231
ISBN 13: 9786139838233
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. Correlation based approach for hiding sensitive items in data mining | A novel approach for Privacy Preserving Data Mining | Kuncham Sreenivasa Rao (u. a.) | Taschenbuch | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139838233 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Codice articolo 120508695
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Correlation based approach for hiding sensitive items in data mining
Editore:
LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6139838231
ISBN 13: 9786139838233
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main goal of data mining is to extract high level or hidden information from large databases. Along with the advantage of extracting useful pattern, it also poses threats of revealing user's sensitive information. We can hide sensitive information of the user by using privacy preservation data mining(PPDM). In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. As association rule is a key tool for finding such patterns, certain association rules can be categorized as sensitive if its disclosure risk is above some given specified threshold. Most privacy preserving data mining approaches use support and confidence. Author in this book proposed correlation based approach which uses measures other than support and confidence such as correlation among items in sensitive itemsets to hide the sensitive frequent itemsets. Columns in dataset having a specified correlation threshold value are considered for hiding process. This mechanism is called Pearson's correlation coefficient weighing mechanism which maintains the trade off between privacy and acuuracy.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 168 pp. Englisch. Codice articolo 9786139838233
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Correlation based approach for hiding sensitive items in data mining : A novel approach for Privacy Preserving Data Mining
Editore:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139838231
ISBN 13: 9786139838233
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main goal of data mining is to extract high level or hidden information from large databases. Along with the advantage of extracting useful pattern, it also poses threats of revealing user¿s sensitive information. We can hide sensitive information of the user by using privacy preservation data mining(PPDM). In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases. As association rule is a key tool for finding such patterns, certain association rules can be categorized as sensitive if its disclosure risk is above some given specified threshold. Most privacy preserving data mining approaches use support and confidence. Author in this book proposed correlation based approach which uses measures other than support and confidence such as correlation among items in sensitive itemsets to hide the sensitive frequent itemsets. Columns in dataset having a specified correlation threshold value are considered for hiding process. This mechanism is called Pearson¿s correlation coefficient weighing mechanism which maintains the trade off between privacy and acuuracy. Codice articolo 9786139838233
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