基于公共模式检测的数据曲线聚类
Data Curves Clustering Using Common Patterns Detection
摘要：
在过去的几十年里，我们经历了人类产生的积累数据的巨大扩展。每天都有大量的智能设备，通常通过互联网互连，产生大量的真实值数据集。时间序列表示来自完全不相关领域的数据集，如金融，天气，医疗应用，交通控制等，在人类日常生活中变得越来越重要。分析和聚类这些时间序列，或者一般来说任何类型的曲线，对于一些人类活动来说都是至关重要的。在当前的文件中，新的曲线聚类使用共同模式（3CP）的方法，它适用于重复模式检测算法，以便聚类序列根据它们的形状和时间序列，数据曲线，最终任何类型的离散序列之间的共同模式的相似性。为此目的，最长预期重复模式减少后缀阵列（LERP-RSA）数据结构已经与所有重复模式检测（ARPaD）算法结合使用，以便对数据曲线之间的相似性执行高度准确和有效的检测，所述相似性可以用于聚类目的并且还提供附加的灵活性和特征。
英文摘要：
For the past decades we have experienced an enormous expansion of the accumulated data that humanity produces. Daily a numerous number of smart devices, usually interconnected over internet, produce vast, real-values datasets. Time series representing datasets from completely irrelevant domains such as finance, weather, medical applications, traffic control etc. become more and more crucial in human day life. Analyzing and clustering these time series, or in general any kind of curves, could be critical for several human activities. In the current paper, the new Curves Clustering Using Common Patterns (3CP) methodology is introduced, which applies a repeated pattern detection algorithm in order to cluster sequences according to their shape and the similarities of common patterns between time series, data curves and eventually any kind of discrete sequences. For this purpose, the Longest Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure has been used in combination with the All Repeated Patterns Detection (ARPaD) algorithm in order to perform highly accurate and efficient detection of similarities among data curves that can be used for clustering purposes and which also provides additional flexibility and features.
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。
