带邻近作用的高增益率co-location模式挖掘

大多数空间co-location模式挖掘将距离阈值作为衡量不同对象实例间邻近关系的标准，进而挖掘出频繁co-location模式，并没有考虑具有邻近关系的实例间的相互影响和模式的增益率问题。在空间co-location模式挖掘过程中，引入实例间的相互作用率和对象的季均收益，定义了对象作用率、套间总收益和增益率等概念，并提出挖掘高增益率co-location模式的基础算法（NAGA）和有效的剪枝算法（NAGA_JZ）。最后通过大量的实验来验证基础算法的正确性和实用性，并对基础算法和剪枝算法的挖掘效率进行了对比，验证了剪枝算法的高效性。

Mining high gain rate co-location patterns with neighboring effection

For most spatial co-location pattern mining methods, distance threshold is used as a standard to measure the neighboring relation among instances of different objects, then to mine frequent co-location patterns, but the interation between instances with neighboring relations and the gain rate of patterns are not considered. In the spatial co-location patterns mining process, by introducing the interation rate between instances and the seasonal average income of objects, the concepts of object effect rate, suite total income and gain rate were defined, and a basic algorithm named NAGA and an efficient pruning algorithm named NAGA_JZ for mining high gain rate co-location patterns were put forward. Finally, a large number of experiments were carried out to verify the correctness and practicability of the basic algorithm, and the mining efficiency of the basic algorithm and the pruning algorithm were compared. The experimental results prove the high efficiency of the pruning algorithm.