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Revisiting Generic Bases of Association Rules

机译:重新审视关联规则的通用基础

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As a side effect of unprecedented amount of digitization of data, classical retrieval tools found themselves unable to go further beyond the tip of the Iceberg. Data Mining in conjunction with the Formal Concept Analysis, is a clear promise to furnish adequate tools to do so and specially to be able to derive concise generic and easy understandable bases of "hidden" knowledge, that can be reliable in a decision making process. In this paper, we propose to revisit the notion of association rule redundancy and to present sound inference axioms for deriving all association rules from generic bases of association rules.
机译:作为数据的数字化数量的副作用,经典检索工具发现自己无法进一步超越冰山尖端。数据挖掘与正式的概念分析结合,是一种明确的承诺,提供适当的工具,并专门能够衍生简明的通用和易于理解的“隐藏”知识的基础,这可以在决策过程中可靠。在本文中,我们建议重新审视关联规则冗余的概念,并呈现出于从关联规则的通用基础中获取所有关联规则的声音推理公理。

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