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Titlebook: Observational Calculi and Association Rules; Jan Rauch Book 2013 Springer-Verlag Berlin Heidelberg 2013 Association Rules.Computational In

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Introduction,ion to market basket analysis [1]. The goal of market basket analysis is to better understand the purchase behavior of customers in supermarkets. Transaction data recorded by point-of-sale systems in supermarkets is analysed. We assume there is a set . = {.., …, .. } of possible items of goods and s
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Data Matrices matrix. An example of such a data matrix with general values is given in Sect. 2.1. However, we are interested only in a simple form of data matrices. This means that we consider data matrices with positive integer numbers as their only values, see Sect. 2.2. Association rules were defined in [1] a
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4ft-quantifiersion to the GUHA procedure ASSOC. These are introduced in Sect. 4.1. There is also a possibility to use various measures of interestingness of association rules together with suitable thresholds for their values to define additional 4ft-quantifiers. An overview of well known measures of interestingne
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Useful Resultsf 4ft-quantifiers is defined by a relatively simple condition concerning frequencies from 4ft-tables. However, the proof that a particular 4ft-quantifier belongs to a particular class of 4ft-quantifiers sometimes requires a relatively complex computation. This is especially true for statistically mo
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Implicational RulesB-quantifier → . with confidence . and support . which is used for analysis of market basket [1] and shortly mentioned in Sect. 1.1 is neither .-independent nor implicational. This leads to notions of weakly implicational quantifiers and rules, see Sect. 7.1. Several practically important implicatio
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Deduction Rules truthfulness of a given association rule implies truthfulness of an additional rule with the same 4ft-quantifier. Criteria of soundness of such deductions rules are found for important classes of 4ft-quantifiers. It is shown that the question of soundness of a given deduction rule can be converted
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