Colocation pattern mining finds a set of features whose instances frequently appear nearby in the same geographical space. Most of the existing algorithms for colocation patterns find nearby objects by a user-provided single-distance threshold. The value of the distance threshold is data specific and choosing a suitable distance for a user is not easy. In most real-world scenarios, it is rather meant to define spatial proximity by a distance range. It also provides flexibility to observe the change in the colocation patterns with distance and interprets the result better. Algorithms for mining colocations with a single distance threshold cannot be applied directly to the range of distances due to the computational overhead. We identify several structural properties of the collocation patterns and use them to propose an efficient single-pass colocation mining algorithm for distance range query, namely $Range-CoMine$. We compare the performance of the $Range-CoMine$ with adapted versions of the famous Join-less colocation mining approach using both real-world and synthetic data sets and show that $Range-CoMine$ outperforms the other algorithms.

共置模式挖掘发现了一组特征，这些特征的实例经常出现在同一地理空间附近。大多数现有的协同定位模式算法通过用户提供的单距离阈值找到附近的对象。距离阈值的值是数据特定的，为用户选择合适的距离并不容易。在大多数现实世界的场景中，它更像是通过距离范围来定义空间接近度。它还提供了灵活性来观察协同定位模式随距离的变化，并更好地解释结果。由于计算开销，用于挖掘具有单个距离阈值的协同定位的算法不能直接应用于距离范围。$R\mathrm{一种}nG\mathrm{电子}-Co米\mathrm{一世}n\mathrm{电子}$. 我们比较的性能$R\mathrm{一种}nG\mathrm{电子}-Co米\mathrm{一世}n\mathrm{电子}$使用真实世界和合成数据集的著名的 Join-less 托管挖掘方法的改编版本，并表明$R\mathrm{一种}nG\mathrm{电子}-Co米\mathrm{一世}n\mathrm{电子}$优于其他算法。