Data Quality Measurement on Categorical Data Using Genetic Algorithm
Data quality on categorical attribute is a difficult problem that has not received as much attention as numerical counterpart. The authors' basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining. Association rule mining problems can be considered as a multi objective problem rather than as a single objective one. The main area of concentration was the rules generated by association rule mining using genetic algorithm. 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.