A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge

Abstract

Discovering knowledge from data means finding useful patterns in data, this process has increased the opportunity and challenge for businesses in the big data era. Meanwhile, improving the quality of the discovered knowledge is important for making correct decisions in an unpredictable environment. Various models have been developed in the past; however, few used both data quality and prior knowledge to control the quality of the discovery processes and results. In this paper, a multi-objective model of knowledge discovery in databases is developed, which aids the discovery process by utilizing prior process knowledge and different measures of data quality. To illustrate the model, association rule mining is considered and formulated as a multi-objective problem that takes into account data quality measures and prior process knowledge instead of a single objective problem. Measures such as confidence, support, comprehensibility and interestingness are used. A Pareto-based integrated multi-objective Artificial Bee Colony (IMOABC) algorithm is developed to solve the problem. Using well-known and publicly available databases, experiments are carried out to compare the performance of IMOABC with NSGA-II, MOPSO and Apriori algorithms, respectively. The computational results show that IMOABC outperforms NSGA-II, MOPSO and Apriori on different measures and it could be easily customized or tailored to be in line with user requirements and still generates high-quality association rules.