A novel method for spatial frequent items query based on concept lattice

The popularization of 5G technology and the development of mobile network devices have given spatial textual dataset more dimensions, which means that the spatial text datasets recording geographical objects are given multiple source and attributes. Data mining on these datasets has become a meaningful work. Top-k spatial keyword query as a common research using spatial textual big data, after years of development, people have proposed a large number of index framework to achieve query. However, previous work often only focused on location and small amount of text, ignoring the associations between objects in spatial textual big data. In order to mine the knowledge contained in the dataset, we propose a Top-k Frequent spatial Keyword Query (TkFKQ) algorithm to index the frequent items in spatial textual dataset. In order to achieve this index, we design an index framework for knowledge mining of spatial textual data sets. The framework combines R-tree with concept lattice for TkFKQ. A large number of comparative experiments are carried out on the real data set to evaluate the method.