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ACSP-tree: A tree structure for mining behavioral patterns from wireless sensor networks

机译：ACSP树：一种树结构，用于从无线传感器网络中挖掘行为模式

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WSNs generates a large amount of data in the form of stream and mining knowledge from the stream of data can be extremely useful. Association rules mining, from the sensor data, has been studied in recent literature. However, sensor association rules mining often produces a huge number of rules, but most of them either are redundant or fail to reflect the true correlation relationship among data objects. In this paper, we address this problem and propose mining of a new type of sensor behavioral pattern called associated-correlated sensor patterns. The proposed behavioral patterns capture not only association-like co-occurrences but also the substantial temporal correlations implied by such co-occurrences in the sensor data. Here, we also use a prefix tree-based structure called associated-correlated sensor pattern-tree (ACSP-tree), which facilitates frequent pattern (FP) growth-based mining technique to generate all associated-correlated patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our approach is time and memory efficient in finding associated-correlated patterns than the existing most efficient algorithms.
机译：WSN以流的形式生成大量数据，从数据流中挖掘知识非常有用。在最近的文献中已经研究了从传感器数据挖掘关联规则的方法。但是，传感器关联规则挖掘通常会产生大量规则，但是大多数规则要么是多余的，要么无法反映数据对象之间的真实关联关系。在本文中，我们解决了这个问题，并提出了一种新型的传感器行为模式，即关联相关传感器模式的挖掘。所提出的行为模式不仅捕获类似关联的共现，而且捕获传感器数据中此类共现所隐含的实质性时间相关性。在这里，我们还使用了基于前缀树的结构，称为关联相关传感器模式树（ACSP-tree），该结构有助于基于频繁模式（FP）增长的挖掘技术从WSN数据中仅生成一个相关联的所有模式扫描传感器数据库。广泛的性能研究表明，与现有最有效的算法相比，我们的方法在查找关联相关模式方面更节省时间和内存。

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