Data Quality Measurement With Threshold Using Genetic Algorithm

Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining. We purpose a Genetic Algorithm to generate high quality Association Rules with four metrics they are confidence, completeness, interestingness and comprehensibility. These metrics are combined as an objective fitness function. Fitness function evaluates the quality of each rule. The advantage of using genetic algorithm is to discover high level prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithm often used in data mining. Association Rule Mining is one of the most applicable techniques in data mining. Association rules that satisfy threshold specified by the user are referred to as strong association rules and a considered interesting.