Formal structures for data mining, knowledge discovery and communication in a knowledge management environment

摘要

Abstract. Current Business Intelligence (BI) initiatives customize DM-KDD techniques into business analytics, which cannotnbe used in applications other than business. A review of current methodology at the strategic level of the KM/KDD domainsnindicates that there exists no general formal framework which can be adopted in new applications, or new application areas.nThere are no established procedures for the domain expert to express their prior knowledge, understanding and aims in a waynwhich can be linked to KDD/DMM processes and subsequent deployment of discovered knowledge. It is suggested that thensequential life-cycle project-management approach of CRISP-DM needs to be complemented by a dynamic interactive view ofna conceptual data/information/knowledge hierarchy in the KM context. It is also suggested that a graphical/visual knowledgenrepresentation framework needs to be developed as the basis of a knowledge and discovery and communication frameworkn(KDCF).nA review of the limitations in DM methodology at the technical/technological level leads to the conclusion that there isnno coherent DM methodology to guide the choice of models and their evaluation, that the DM discipline is fractionated, andnthat the fundamental search and sampling paradigms have been insufficiently utilized in DM development. It is proposed thatndevelopment of linked data and model ontologies, together with a DM-epistemology, and associated with full exploitation ofnsearch and sampling could lead to improved cohesion and efficacy of the DM discipline.