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dc.contributor.authorShao, Qun*
dc.contributor.authorRowe, Raymond C.*
dc.contributor.authorYork, Peter*
dc.date.accessioned2009-09-14T10:19:16Z
dc.date.available2009-09-14T10:19:16Z
dc.date.issued2008
dc.identifier.citationShao, Q., Rowe, C. and York, P. (2008). Data mining of fractured experimental data using neurofuzzy logic-discovering and integrating knowledge hidden in multiple formulation databases for a fluid-bed granulation process. Journal of Pharmaceutical Sciences. Vol. 97, No. 6, pp. 2091-2101.en
dc.identifier.urihttp://hdl.handle.net/10454/3439
dc.descriptionNoen
dc.description.abstractIn the pharmaceutical field, current practice in gaining process understanding by data analysis or knowledge discovery has generally focused on dealing with single experimental databases. This limits the level of knowledge extracted in the situation where data from a number of sources, so called fractured data, contain interrelated information. This situation is particularly relevant for complex processes involving a number of operating variables, such as a fluid-bed granulation. This study investigated three data mining strategies to discover and integrate knowledge "hidden" in a number of small experimental databases for a fluid-bed granulation process using neurofuzzy logic technology. Results showed that more comprehensive domain knowledge was discovered from multiple databases via an appropriate data mining strategy. This study also demonstrated that the textual information excluded in individual databases was a critical parameter and often acted as the precondition for integrating knowledge extracted from different databases. Consequently generic knowledge of the domain was discovered, leading to an improved understanding of the granulation process.en
dc.language.isoenen
dc.relation.isreferencedbyhttp://dx.doi.org/10.1002/jps.21098en
dc.subjectPharmaceutical technologyen
dc.subjectProcessen
dc.subjectGranulationen
dc.subjectDatabaseen
dc.subjectFormulationen
dc.subjectKnowledgeen
dc.subjectData miningen
dc.titleData mining of fractured experimental data using neurofuzzy logic-discovering and integrating knowledge hidden in multiple formulation databases for a fluid-bed granulation process.en
dc.status.refereedYesen
dc.typeArticleen
dc.type.versionNo full-text available in the repositoryen


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