Andere Kunden interessierten sich auch für
![Reversible Data Hiding Techniques]( "Reversible Data Hiding Techniques")
Reversible Data Hiding Techniques37,99 €![Strategic decision making: a data mining approach]( "Strategic decision making: a data mining approach")
Strategic decision making: a data mining approach51,99 €![Interaction Data Reuse Approach for Intelligent Tutor Development]( "Interaction Data Reuse Approach for Intelligent Tutor Development")
Interaction Data Reuse Approach for Intelligent Tutor Development17,99 €![Investigation on Impact of Reservation on Enrollment using Data Mining]( "Investigation on Impact of Reservation on Enrollment using Data Mining")
Investigation on Impact of Reservation on Enrollment using Data Mining26,99 €![Data Mining Techniques]( "Data Mining Techniques")
Data Mining Techniques29,99 €![Temperature & Humidity Prediction Using Data Mining]( "Temperature & Humidity Prediction Using Data Mining")
Temperature & Humidity Prediction Using Data Mining26,99 €![Data Mining in Life Insurance Sector of India: A Model Based Approach]( "Data Mining in Life Insurance Sector of India: A Model Based Approach")
Data Mining in Life Insurance Sector of India: A Model Based Approach51,99 €
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.