Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases
Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases
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DOI：
10.3390/electronics11101523
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发表时间：
2022-05
期刊：
影响因子：
2.9
通讯作者：
R. U. Kiran;Pamalla Veena;Penugonda Ravikumar;C. Saideep;K. Zettsu;Haichuan Shang;Masashi Toyoda;M. Kitsuregawa;P. K. Reddy
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文献类型：
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作者：
R. U. Kiran;Pamalla Veena;Penugonda Ravikumar;C. Saideep;K. Zettsu;Haichuan Shang;Masashi Toyoda;M. Kitsuregawa;P. K. Reddy
周期性模式挖掘是一种新兴的知识发现技术。大多数以前的方法旨在仅找到那些在数据库中表现出完整（或完美）周期性行为的模式。因此，现有的方法错过了在数据库中表现出部分周期性行为的有趣模式。出于这个动机，本文提出了一种新的模型来查找时态数据库中可能存在的部分周期性模式。还提出了一种有效的模式增长算法，称为部分周期性模式增长（3P-growth），它可以有效地找到数据库中的所有所需模式。对真实世界和合成数据库的大量实验表明，我们的算法不仅在内存和运行时方面高效，而且具有高度可扩展性。最后，我们的模式的有效性通过两个案例研究得到证明。在第一个案例研究中，我们的模型被用来识别日本的高污染地区。在第二个案例研究中，我们的模型用于识别人们经常面临交通拥堵的路段。
Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.