Autonomous decision-making: a data mining approach

Authors:
A. Kusiak;J. A. Kern;K. H. Kernstine;B. T.L. Tseng
Affiliations:
Lab. of Intelligent Syst., Iowa Univ., Iowa City, IA, USA;-;-;-
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2000

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Abstract

The researchers and practitioners of today create models, algorithms, functions, and other constructs defined in abstract spaces. The research of the future will likely be data driven. Symbolic and numeric data that are becoming available in large volumes will define the need for new data analysis techniques and tools. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. In this paper, a novel approach for autonomous decision-making is developed based on the rough set theory of data mining. The approach has been tested on a medical data set for patients with lung abnormalities referred to as solitary pulmonary nodules (SPNs). The two independent algorithms developed in this paper either generate an accurate diagnosis or make no decision. The methodology discussed in the paper depart from the developments in data mining as well as current medical literature, thus creating a variable approach for autonomous decision-making.