Selective Colligation and Selective Scrambling for Privacy Preservation in Data Mining
The work is to enhance the time efficiency in retrieving the data from enormous bank database. The major drawback in retrieving data from large databases is time delay. This time hindrance is owed as the already existing method (SVM), Abstract Data Type (ADT) tree pursues some elongated Sequential...
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| Main Authors: | , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2018-05-01.
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| Online Access: | Get fulltext |
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| LEADER | 02322 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs11095_8406 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a M. V., Ishwarya |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Kumar, K. Ramesh |e author |
| 245 | 0 | 0 | |a Selective Colligation and Selective Scrambling for Privacy Preservation in Data Mining |
| 260 | |b Institute of Advanced Engineering and Science, |c 2018-05-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/11095 | ||
| 520 | |a The work is to enhance the time efficiency in retrieving the data from enormous bank database. The major drawback in retrieving data from large databases is time delay. This time hindrance is owed as the already existing method (SVM), Abstract Data Type (ADT) tree pursues some elongated Sequential steps. These techniques takes additional size and with a reduction of speed in training and testing. Another major negative aspect of these techniques is its Algorithmic complexity. The classification algorithms have five categories. They are ID3, k-nearest neighbour, Decision tree, ANN, and Naïve Bayes algorithm. To triumph over the drawbacks in SVM techniques, we worn a technique called Naïve Bayes Classification (NBC) Algorithm that works in parallel manner rather than sequential manner. For further enhancement we commenced a Naïve Bayes updatable algorithm which is the advanced version of Naïve Bayes classification algorithm. Thus the proposed technique Naïve bayes algorithm ensures that miner can mine more efficiently from the enormous database. | ||
| 540 | |a Copyright (c) 2018 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a SVM; ANN; Bayesian Algorithm; Privacy Preservation; Data Mining | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 10, No 2: May 2018; 778-785 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v10.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/11095/8406 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/11095/8406 |z Get fulltext |