DIR-ST $^2$：用时空文本信息描述不精确区域
DIR-ST$^2$: Delineation of Imprecise Regions Using Spatio--Temporal--Textual Information
摘要：
在文献中，不精确区域是指没有明确定义边界的地理区域。以前的基于聚类的方法利用空间信息来找到这样的区域。然而，现有的研究存在以下两个问题：选择聚类参数的主观性和包含大部分不期望的区域（即，大量噪声点）。为了克服这些问题，我们提出了DIR-ST $^2$，这是一种新颖的框架，用于通过迭代执行基于密度的聚类（即DBSCAN）以及社交媒体上的空间文本信息和时间信息来描绘不精确的区域。具体来说，我们的目标是找到一个合适的半径的圆在迭代DBSCAN过程中使用的逐步减少半径的每次迭代中，从所有产生的集群获得的时间信息被利用。然后，我们提出了一个有效的和自动化的算法划定不精确的区域，通过层次聚类。实验结果表明，凭借显着的噪声减少在该地区，我们的DIR-ST $^2$方法优于国家的最先进的方法，采用单类支持向量机的$\mathcal{F}_1$分数从比较与精确定义的地区被视为地面真理，并返回明显更好的描绘不精确的地区。DIR-ST $^2$的计算复杂性也被解析和数值示出。
英文摘要：
An imprecise region is referred to as a geographical area without a clearly-defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters and the inclusion of a large portion of the undesirable region (i.e., a large number of noise points). To overcome these problems, we present DIR-ST$^2$, a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio--textual information but also temporal information on social media. Specifically, we aim at finding a proper radius of a circle used in the iterative DBSCAN process by gradually reducing the radius for each iteration in which the temporal information acquired from all resulting clusters are leveraged. Then, we propose an efficient and automated algorithm delineating the imprecise region via hierarchical clustering. Experiment results show that by virtue of the significant noise reduction in the region, our DIR-ST$^2$ method outperforms the state-of-the-art approach employing one-class support vector machine in terms of the $\mathcal{F}_1$ score from comparison with precisely-defined regions regarded as a ground truth, and returns apparently better delineation of imprecise regions. The computational complexity of DIR-ST$^2$ is also analytically and numerically shown.
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。
