Abstract
In this paper, we introduce an uncertain data mining driven model for knowledge discovery in chemical database. We aim at discovering relationship between molecule characteristics and properties using uncertain data mining tools. In fact, we intend to predict the Critical Micelle Concentration (CMC) property based on a molecule characteristics. To do so, we develop a likelihood-based belief function modelling approach to construct evidential database. Then, a mining process is developed to discover valid association rules. The prediction is performed using association rule fusion technique. Experiments were conducted using a real-world chemical databases. Performance analysis showed a better prediction outcome for our proposed approach in comparison with several literature-based methods.
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Notes
- 1.
An amphiphilic molecule is chemical compound possessing both hydrophilic (water-loving, polar) and lipophilic (fat-loving) properties.
- 2.
Each subset A of \(2^{\varTheta }\), fulfilling \(m(A)>0\), is called a focal element.
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Acknowledgement
This work was performed, in partnership with the SAS PIVERT, within the frame of the French Institute for the Energy Transition (Institut pour la Transition Energétique (ITE) P.I.V.E.R.T. (www.institut-pivert.com) selected as an Investment for the Future (“Investissements d’Avenir”). This work was supported, as part of the Investments for the Future, by the French Government under the reference ANR-001-01.
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Samet, A. et al. (2016). Predictive Model Based on the Evidence Theory for Assessing Critical Micelle Concentration Property. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-40596-4_43
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