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Data Mining Using Fuzzy Decision Trees: An Exposition from a Study of Public Services Strategy in the USA

Data Mining Using Fuzzy Decision Trees: An Exposition from a Study of Public Services Strategy in the USA
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Author(s): Malcolm J. Beynon (Cardiff University, UK) and Martin Kitchener (Cardiff Business School, UK)
Copyright: 2010
Pages: 20
Source title: Data Mining in Public and Private Sectors: Organizational and Government Applications
Source Author(s)/Editor(s): Antti Syvajarvi (University of Lapland, Finland) and Jari Stenvall (Tampere University, Finland)
DOI: 10.4018/978-1-60566-906-9.ch003

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Abstract

The chapter exposits the strategies employed by the public long-term care systems operated by each U.S. state government. The central technique employed in this investigation is fuzzy decision trees (FDTs), producing a rule-based classification system using the well known soft computing methodology of fuzzy set theory. It is a timely exposition, with the employment of set-theoretic approaches to organizational configurations, including the fuzzy set representation, starting to be discussed. The survey details considered, asked respondents to assign each state system to one of the three ‘orientations to innovation’ contained within Miles and Snows’ (1978) classic typology of organizational strategies. The instigated aggregation of the experts’ opinions adheres to the fact that each long-term care system, like all organizations, is “likely to be part prospector, part defender, and part reactor, reflecting the complexity of organizational strategy”. The use of FDTs in the considered organization research problem is pertinent since the linguistic based fuzzy decision rules constructed, open up the ability to understand the relationship between a state’s attributes and their predicted position in a general strategy domain - the essence of data mining.

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