Discovering spatial co-location patterns is a process of finding groups of distinct spatial features whose instances are frequently located together in spatial proximity. A co-location pattern is prevalent if its participation index is no less than a minimum prevalence threshold given by users. Most of the existing algorithms are very sensitive to the prevalence threshold, when users change the prevalence threshold, these algorithms have to re-collect table instances and re-calculate participation indexes of all patterns for mining the prevalent patterns that users expect to acquire. To tackle this issue, we propose an overlapping clique-based spatial co-location pattern mining framework (OCSCP). In our framework, we design a two-level filter mechanism with the first level is a feature type filter and the second level is a neighboring instance filter. By employing the mechanism, under a certain neighbor relationship, spatial instances are divided into a set of overlapping cliques and each clique is also a co-location instance of a pattern. And then, a co-location pattern hash map structure is designed to store table instances of patterns based on these overlapping cliques. The participation index of each pattern can be fast and directly calculated from the hash map structure. Thus, when the prevalence threshold is changed, the proposed framework does not need to re-gather table instances, and the mining result can be adaptively and quickly given to users. The proposed algorithms are performed on both synthetic and real-world data sets to demonstrate that our algorithms can rapidly respond to user requirements compared to the previous algorithms.

发现空间共处一地模式是寻找一组不同空间特征的过程，这些空间特征的实例通常在空间上相邻在一起。如果共处一地模式的参与指数不小于用户给出的最小流行阈值，则该共处一地模式是普遍的。现有的大多数算法对流行度阈值非常敏感，当用户更改流行度阈值时，这些算法必须重新收集表实例并重新计算所有模式的参与索引，以挖掘用户期望获取的流行模式。为了解决这个问题，我们提出了一个基于集团的重叠式空间共置模式挖掘框架（OCSCP）。在我们的框架中 我们设计了一个两级过滤器机制，第一级是特征类型过滤器，第二级是相邻实例过滤器。通过采用该机制，在一定的邻居关系下，空间实例被划分为一组重叠的集团，并且每个集团也是模式的共置实例。然后，一个共置模式的哈希表结构被设计为基于这些重叠的集团存储模式的表实例。每个图案的参与指数可以快速且直接从哈希图结构中计算得出。因此，当流行度阈值改变时，所提出的框架不需要重新收集表实例，并且可以自适应地并且快速地将挖掘结果提供给用户。