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Generalizing the notion of confidence

机译：概括信心的概念

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In this paper, we explore extending association analysis to non-traditional types of patterns and non-binary data by generalizing the notion of confidence. We begin by describing a general framework that measures the strength of the connection between two association patterns by the extent to which the strength of one association pattern provides information about the strength of another. Although this framework can serve as the basis for designing or analyzing measures of association, the focus in this paper is to use the framework as the basis for extending the traditional concept of confidence to error-tolerant itemsets (ETIs) and continuous data. To that end, we provide two examples. First, we (1) describe an approach to defining confidence for ETIs that preserves the interpretation of confidence as an estimate of a conditional probability, and (2) show how association rules based on ETIs can have better coverage (at an equivalent confidence level) than rules based on traditional itemsets. Next, we derive a confidence measure for continuous data that agrees with the standard confidence measure when applied to binary transaction data. Further analysis of this result exposes some of the important issues involved in constructing a confidence measure for continuous data.
机译：在本文中，我们通过概括置信度的概念，探索将关联分析扩展到非传统类型的模式和非二进制数据。我们从描述一个通用框架开始，该框架通过一个关联模式的强度提供有关另一个关联模式的信息的程度来度量两个关联模式之间的连接强度。尽管此框架可以用作设计或分析关联度量的基础，但本文的重点是使用该框架作为将传统的置信度概念扩展到容错项目集（ETI）和连续数据的基础。为此，我们提供了两个示例。首先，我们（1）描述了一种定义ETI的置信度的方法，该方法保留了对置信度的解释，作为对条件概率的估计，并且（2）显示了基于ETI的关联规则如何能够更好地覆盖（在同等置信度水平下）而不是基于传统项目集的规则。接下来，我们推导连续数据的置信度，该置信度在应用于二进制交易数据时与标准置信度一致。对该结果的进一步分析揭示了构建连续数据置信度度量所涉及的一些重要问题。

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