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Enumeration Tree Based Emerging Patterns Mining by Using Two Different Supports

机译:基于两个不同支持的基于枚举树的新兴模式挖掘

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Recently, the analysis of power load in the electrical industry has becomes an important element for the concern of customer safety. In power system related studies, data mining techniques are used in power load analysis and they can help decision making in the electrical industry. In this paper, for using emerging patterns to define and analyze the significant difference of safe and non-safe power load lines, and identifying which line is potentially unsafe, we proposed an incremental TFP-tree algorithm for mining emerging patterns that can search efficiently within memory limitation. Especially, the use of two different minimum supports makes the algorithm possible to mine most number of emerging patterns and efficiently handle the incrementally increased, large size of data sets such as power consumption data.
机译:最近,出于对客户安全的关注,对电气行业中的电力负载进行分析已成为重要的内容。在与电力系统相关的研究中,数据挖掘技术用于电力负载分析中,它们可以帮助电力行业进行决策。在本文中,为了使用新兴模式来定义和分析安全和非安全电力线的显着差异,并确定哪条线可能不安全,我们提出了一种增量式TFP-tree算法,用于挖掘可以在内部有效搜索的新兴模式。内存限制。特别是,两个不同的最小支持的使用使该算法可以挖掘最多数量的新兴模式,并有效地处理诸如功耗数据之类的数据集的增量增长,大尺寸。

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