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- W1862187937 abstract "Data Mining explanatory models must deal with relevance: how values of different data items are relevant to the values of other data items. But to be able to construct explanatory models, and in particular causal explanatory models, we must do so by first understanding irrelevance and exactly how irrelevance plays a role in explanatory models. The reason is that the conditional irrelevance or conditional no influence relation defines the boundaries of the ballpark within which an explanatory model lives.This paper reviews the theory of no influence in the mathematical relation data structure. We discuss the relationship this theory has to graphical models and we define a coefficient of no influence and give a method for the estimation of its p-value." @default.
- W1862187937 created "2016-06-24" @default.
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- W1862187937 date "2013-01-01" @default.
- W1862187937 modified "2023-09-26" @default.
- W1862187937 title "Relation Decomposition: The Theory" @default.
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- W1862187937 doi "https://doi.org/10.1007/978-3-642-39712-7_24" @default.
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