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A Test Paradigm for Detecting Changes in TransactionalData Streams

机译：用于检测TransactionalData流中的更改的测试范例

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摘要

A pattern is considered useful if it can be used to help a person to achieve his goal. Mining data streams for useful patterns is important in many applications. However, data stream can change their behavior over time and, when significant change occurs, much harm is done to the mining result if it is not properly handled. In the past, there have been many studies mainly on adapting to changes in data streams. We contend that adapting to changes is simply not enough. The ability to detect and characterize change is also essential in many applications, for example intrusion detection, network traffic analysis, data streams from intensive care units etc. Detecting changes is nontrivial. In this paper, an online algorithm for change detection in utility mining is proposed. In order to provide a mechanism for making quantitative description of the detected change, we adopt the statistical test. We believe there is the opportunity for an immensely rewarding synergy between data mining and statistic. Different statistical significance tests are evaluated and our study shows that the Chi-square test is the most suitable for enumerated or count data (as is the case for high utility itemsets). We demonstrate the effectiveness of the proposed method by testing it on IBM QUEST market-basket data.
机译：如果某种模式可以用来帮助一个人实现其目标，那么它就被认为是有用的。在许多应用程序中，为有用的模式挖掘数据流很重要。但是，数据流会随着时间的流逝而改变其行为，当发生重大变化时，如果处理不当，会对挖掘结果造成很大的伤害。过去，有许多研究主要针对适应数据流的变化。我们认为，仅仅适应变化是不够的。在许多应用中，检测和表征变化的能力也是必不可少的，例如入侵检测，网络流量分析，重症监护室的数据流等。检测变化并非易事。本文提出了一种基于效用挖掘的在线变化检测算法。为了提供一种对检测到的变化进行定量描述的机制，我们采用了统计检验。我们认为，数据挖掘和统计之间存在巨大的协同增效机会。对不同的统计显着性检验进行了评估，我们的研究表明，卡方检验最适合枚举或计数数据（如高实用项集的情况）。我们通过在IBM QUEST市场购物数据上对其进行测试来证明该方法的有效性。

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