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Revisiting Generic Bases of Association Rules

机译:重温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:作为前所未有的大量数据数字化的副作用,传统的检索工具发现自己无法超越冰山一角。数据挖掘与形式概念分析相结合,是一个明确的承诺,可以提供足够的工具来做到这一点,特别是能够派生出简洁明了且易于理解的“隐藏”知识基础,这些知识在决策过程中是可靠的。在本文中,我们提议重新审视关联规则冗余的概念,并提出合理的推理公理,以从关联规则的通用基础上推导所有关联规则。

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