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Data Quality Mining using Genetic Algorithm
Sufal Das, Banani Saha
Pages - 105 - 112     |    Revised - 05-05-2009     |    Published - 18-05-2009
Volume - 3   Issue - 2    |    Publication Date - April 2009  Table of Contents
Data Quality, Genetic Algorithms, Association Rule Mining, Multi-objective Optimization
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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Mr. Sufal Das
- India
Dr. Banani Saha
Calcutta University - India

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