论文标题
事件聚类和事件序列对预期频率的表征
Event Clustering & Event Series Characterization on Expected Frequency
论文作者
论文摘要
我们提出了一种适用于一维数据的有效聚类算法，例如一系列时间戳。给定预期的频率$ΔT^{ - 1} $，我们引入了$ \ Mathcal {o}（n）$ - 表征$ n $事件的有效方法，该方法由订购的一系列时间戳$ t_1，t_1，t_2，\ dots，\ dots，t_n $。实际上，该方法对例如确定“丢失”数据的时间间隔或找到“隔离事件”。此外，我们定义了通过将$ΔT$更改为例如确定物联网服务的质量。
We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of timestamps. Given an expected frequency $ΔT^{-1}$, we introduce an $\mathcal{O}(N)$-efficient method of characterizing $N$ events represented by an ordered series of timestamps $t_1,t_2,\dots,t_N$. In practice, the method proves useful to e.g. identify time intervals of "missing" data or to locate "isolated events". Moreover, we define measures to quantify a series of events by varying $ΔT$ to e.g. determine the quality of an Internet of Things service.
