Vector-Degree: A General Similarity Measure for Co-location Patterns

Co-location pattern mining is one of the hot issues in spatial pattern mining. Similarity measures between co-location patterns can be used to solve problems such as pattern compression, pattern summarization, pattern selection and pattern ordering. Although, many researchers have focused on this issue recently and provided a more concise set of co-location patterns based on these measures. Unfortunately, these measures suffer from various weaknesses, e.g., some measures can only calculate the similarity between super-pattern and sub-pattern while some others require additional domain knowledge. In this paper, we propose a general similarity measure for any two co-location patterns. Firstly, we study the characteristics of the co-location pattern and present a novel representation model based on maximal cliques. Then, two materializations of the maximal clique and the pattern relationship, 0-1 vector and key-value vector, are proposed and discussed in the paper. Moreover, based on the materialization methods, the similarity measure, Vector-Degree, is defined by applying the cosine similarity. Finally, similarity is used to group the patterns by a hierarchical clustering algorithm. The experimental results on both synthetic and real world data sets show the efficiency and effectiveness of our proposed method.