时态关系流的增量信息增益挖掘
Incremental Information Gain Mining Of Temporal Relational Streams
摘要：
研究了关系表中具有高信息增益的数据值的挖掘问题。高信息增益可以帮助数据分析师和二级数据采掘算法深入了解关键指标之间的强统计依赖性和因果关系。在本文中，我们将研究的问题，高信息增益识别的情况下，新的记录不断添加到关系的时间关系。我们表明，信息增益可以有效地保持在一个增量的方式，使其能够连续监测高信息增益值。
英文摘要：
This paper studies the problem of mining for data values with high information gain in relational tables. High information gain can help data analysts and secondary data mining algorithms gain insights into strong statistical dependencies and causality relationship between key metrics. In this paper, we will study the problem of high information gain identification for scenarios involving temporal relations where new records are added continuously to the relations. We show that information gain can be efficiently maintained in an incremental fashion, making it possible to monitor continuously high information gain values.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
