Analysis and Implementation of Efficient Association Rules using K-mean and Neuralgas Algorithm
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 14, Issue 5
Abstract
Efficient Privacy Preserving association rule mining has emerged as a latest research issue. In this thesis work, existing algorithms, Increase Support of Left Hand Side and Decrease Support of Right Hand Side are implemented successfully on the real data for Privacy Preserving Association Rule Mining. To provide privacy to sensitive data we also propose and implement a new algorithm .The performance of new algorithm is also compared with existing algorithms on the basis of number of rule pruned. The result show that proposed algorithm is more efficient as it performs privacy preserving mining by pruning more rules. Securing these against unauthorized access to the long-term goal of the database security research base community and the government statistical agencies. Whether data is personal or corporate data, data mining offers the potential to reveal what other regard as sensitive (private). In some cases, it may be of mutual benefit for two parties’ even competitors to be share their data for analysis task. They would like to it will be ensure their own data remains private. In other good words, there is a need to protect sensitive knowledge during a data mining process. For Experimental work, we have used a realistic database of Doctor Patient Evaluation is taken from Medical College
Authors and Affiliations
Mohnish Patel
Keywords
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