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| Data Quality Mining using Genetic Algorithm
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Complete Issue PDF(1.98MB) |
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Volume: 3 Issue: 2 |
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Pages: 62-153 |
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Publication
Date: April 2009 |
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ISSN
(Online): 1985-1553 |
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Pages |
105 - 112 |
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Author(s) |
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Published
Date |
18-05-2009 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Data Quality, Genetic Algorithms, Association Rule Mining, Multi-objective Optimization |
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| Data quality mining (DQM) as a new and promising data mining approach from the academic and the business point of view. Data quality is important to organizations. People use information attributes as a tool for assessing data quality. The goal of DQM is to employ data mining methods in order to detect, quantify, explain and correct data quality deficiencies in very large databases. Data quality is crucial for many applications of knowledge discovery in databases (KDD). In this work, we have considered four data qualities like accuracy, comprehensibility, interestingness and completeness. We have tried to develop Multi-objective Genetic Algorithm (GA) based approach utilizing linkage between feature selection and association rule. The main motivation for using GA in the discovery of high-level prediction rules is that they perform a global search and cope better with attribute interaction that the greedy rule induction algorithms often used in data mining. |
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O. J. Oyelade and O. O. Oyejoke, “Knowledge Discovery from Students’ Result Repository: Association Rule Mining Approach”, International Journal of Computer Science and Security (IJCSS), 4(2), pp. 199 – 207, 2010.
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E. Omara, T. E. Said and M. Mousa, “Employing Neural Networks for Assessment of Data Quality with Emphasis on Data Completeness”, International Journal on Artificial Intelligence and Machine Learning, 11(I), pp. 21--28, 2011.
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M. Awad, “Optimization RBFNNs Parameters Using Genetic Algorithms: Applied on Function Approximation”, International Journal of Computer Science and Security (IJCSS), 4(3), pp. 295 – 307, 2010.
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H. Miao, “A Multi-Operator Based Simulated Annealing Approach for Robot Navigation in Uncertain Environments”, International Journal of Computer Science and Security (IJCSS), 4(1), pp. 50 – 61, 2010.
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R. M. Kumar and Dr. K. Iyakutti. “Application of Genetic algorithms for the prioritization of Association Rules”. IJCA Special Issue on Artificial Intelligence Techniques - Novel Approaches & Practical Applications (3), pp. 1–3, 2011.
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E. Chandra and K. Nandhini, “Knowledge Mining from Student Data”, European Journal of Scientific Research 47(1), pp.156-163, 2010.
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| Sufal Das : Colleagues
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| Banani Saha : Colleagues
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