Abstract
If we are to implement Intelligent Data Analysis into computer systems we must instantiate the essential aspects of scientific method. In the quest to systematically unearth hidden information and bring forth knowledge from complex, noisy and incomplete data, we must be sure that such systems are first able to determine all possible patterns, which are suggestive of such information and knowledge from all possibly relevant, clean and complete data sets.
It will be shown that John Stuart Mill's methods as extrapolated in his ‘System of Logic’ provide such a set of algorithms. The value of all possible relevant data sets is determined prior to the coverage of each of Mill's methods. It is then shown that they are distinct from each other and given any particular data set, only one of the ‘methods’ is applicable, that is that they are disjunct. It is then shown that the methods cover all relevant data sets.
Attention is then directed to some Intelligent Data Analysis systems to determine which of Mill's methods they implement. None of them implement all of them or are aware of the applicability of the methods to the complex task of Intelligent Data Analysis.
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Cornish, T.A.O. (1997). Mill's methods for complete Intelligent Data Analysis. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052830
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DOI: https://doi.org/10.1007/BFb0052830
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