MLCPM-UC:A Multi-level Co-location Pattern Mining Algorithm Based on Uniform Coefficient of Pattern Instance Distribution

Abstract

The spatial co-location pattern is a set of spatial features,and the instances frequently appear together in the spatial region.Due to the correlation and heterogeneity of spatial data,the distribution of co-location instances may appear globally in the whole research area (global co-location pattern),or appear in a local area of the research area (regional co-location pattern),Thus the multi-level co-location pattern mining is proposed.There are two problems with current multi-level co-location pattern mining methods:1)the existing multi-level co-location pattern mining methods ignore the spatial distribution characteristics of patterns and fail to accurately distinguish global and regional co-location patterns;2)the existing multi-level pattern mining method uses global non-prevalent co-location patterns as candidate regional co-location patterns,and the number of candidate patterns is too large.In response to the above problems,firstly,we define the uniform coefficient of the instance distribution of the co-location pattern and consider the pattern distribution in space while considering the pattern prevalence,so as to correctly and efficiently identify the global and regional co-location patterns.Secondly,a novel multi-level co-location pattern mining algorithm is designed based on the uniformity coefficient of the instance distribution of the pattern.In this algorithm,an effective pruning strategy is proposed to improve the efficiency of the algorithm.Finally,extensive experiments are carried out on real and synthetic data sets,which verify the correctness and efficiency of the proposed method.

Keywords

• spatial data mining|multi-level co-location pattern|spatial heterogeneity|uniform coefficient