Title: Knowledge-based semantic discretisation using data mining techniques
Authors: Omprakash Chandrakar; Jatinderkumar R. Saini
Addresses: Uka Tarsadia University, Bardoli, Gujarat, India ' Symbiosis Institute of Computer Studies and Research, Pune, India
Abstract: Certain data mining techniques can be applied only on discretised data. Various studies show significant improvement in certain data mining techniques, when applied on discretised data rather than continuous data. Discretisation methods based on statistical techniques are inadequate in capturing and exploiting the knowledge inherent in data and context of study. To overcome this limitation, we propose a novel knowledge-based semantic discretisation method using data mining techniques, in which discretisation is done based on semantic data. Semantic data is domain knowledge inherent in the data and context of the study. Unlike semantic data mining, no explicit ontology associated with the data for semantic discretisation. Therefore, it's a challenging task to identify, capture, interpret and exploit the semantic data for semantic discretisation. This study presents the novel concept of semantic discretisation. The effectiveness of the proposed methodology is shown by applying it on Pima Indian diabetes dataset.
Keywords: association rule mining; data mining; discretisation; machine learning; Pima Indian diabetes dataset; prediction model; semantic discretisation; type-2 diabetes.
International Journal of Advanced Intelligence Paradigms, 2020 Vol.17 No.3/4, pp.267 - 278
Received: 25 Jul 2016
Accepted: 13 May 2017
Published online: 11 Sep 2020 *