Ontology Based Data Analysing Approach for Actionable Knowledge Discovery

Abstract

In Data Mining, the effectiveness of association rules is limited by the huge quantity of delivered rules. In this manuscript, we propose a new approach to prune and filter discovered rules. An interactive and iterative framework is designed to assist the user along the analyzing task. The manuscript focus on medical data test set for detailed analysis and formation of the ontologies. In the proposed approach, the data set of medical records is entered in the back - end database. The development of associations and the ontology is fully dynamic. In case, any symptom is searched, the proposed approach search from the back end database and creates the ontology that is dynamic in execution. It refers that the unsupervised approach of ontology generation is implemented so that the unbiased results can be achieved. The implementation of proposed work shows the dynamic results in terms of rules found and their effectiveness in the real world scenario. 3 M) which is intended to make data mining workable in supporting decision-making actions in the real world. Current data mining tools and techniques drive a paradigm shift from traditional data-centred hidden pattern mining to domain-driven actionable knowledge discovery (AKD). AKD must cater for domain knowledge and environmental factors, balance technical significance and business expectations from both objective and subjective perspectives and support automatically converting patterns into deliverables in business friendly and operable forms such as actions or rules. In domain-driven framework, data mining analysts and domain-specific business analysts complement each other with regard to in-depth granularity and constrained environment through interactive system support. The involvement of domain experts and their knowledge can assist in developing highly effective domain specific data mining techniques, and reduce the complexity of knowledge discovery. The involvement of domain-related social intelligence into KDD process not only strengthens technical development and performance, but highlights business expectations and actionable capability of the identified results. In Data Mining, the effectiveness of association rules is strappingly limited by the huge quantity of delivered rules. In this manuscript, we propose a new approach to prune and filter discovered rules. An association rule is described as the implication X → Y where X and Y are sets of items and