Stochastic processes and temporal rules

Paul Cotofrei, K. Stoffel
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Abstract

This article tries to give an answer to a fundamental question in temporal data mining: "Under what conditions a temporal rule extracted from an up-to-date temporal data keeps its cofidence/support on future data". A possible solution is given by using, on the one hand, a temporal logic formalism which allows the definition of the main notions (event, temporal rule, confidence) in a formal way and, on the other hand, the stochastic limit theory. Under this probabilistic temporal framework, the equivalence between the existence of the support of a temporal rule and the law of large numbers is systematically analyzed.
随机过程和时间规则
本文试图回答时间数据挖掘中的一个基本问题:“从最新的时间数据中提取的时间规则在什么条件下可以保持对未来数据的信任/支持”。一个可能的解决方案是,一方面使用时间逻辑形式主义,它允许以正式的方式定义主要概念(事件,时间规则,置信度),另一方面,随机极限理论。在此概率时间框架下,系统地分析了时间规则支持的存在性与大数定律的等价性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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