Empowering Data Mining Sciences by Habitual Domains Theory, Part I: The Concept of Wonderful Solution

Moussa Larbani () and Po Lung Yu ()
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Moussa Larbani: Carleton University
Po Lung Yu: University of Kansas

Annals of Data Science, 2020, vol. 7, issue 3, No 1, 373-397

Abstract: Abstract Often big companies and governments fail to produce a successful plan of action in the post-data mining analysis and decision-making stage to face challenging situations, which results in billions of dollars loss. Indeed, this crucial stage is left to the decision makers (DMs) to make decisions based on their experience, available information, mindset and habits without any scientific and systematic approach. In this paper, we present a general formal model for decision-making and new knowledge generation that can be used in post-data mining analysis to derive better decisions. The proposed model is formulated in the framework of Habitual Domains theory. The concepts of optimization in changeable spaces and the related solution, the wonderful solution, are introduced in Part I of the paper. Further, ways on how to find wonderful solutions by expanding DMs Habitual Domains in post-data mining analysis and decision-making are presented with applications in Part II of the paper. The proposed model is a significant departure from the traditional decision models that are based on utility function. It can also be used to empower other sciences such as political sciences, medical sciences, management sciences and research activities in all areas.

Keywords: Post data mining; Habitual domain; Competence set; Wonderful solution (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s40745-020-00290-0

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