Paper Info
Title
Vector-Degree: A General Similarity Measure for Co-location Patterns
Abstract
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.
Year
DOI
Venue
2019
10.1109/ICBK.2019.00045
2019 IEEE International Conference on Big Knowledge (ICBK)
Keywords
Field
DocType
spatial co-location patterns mining, maximal clique, similarity measure, hierarchical clustering
Hierarchical clustering,Common spatial pattern,Automatic summarization,Data set,Domain knowledge,Similarity measure,Cosine similarity,Pattern recognition,Clique,Computer science,Artificial intelligence
Conference
ISBN
Citations 
PageRank 
978-1-7281-4608-9
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Pingping Wu100.34
Lizhen Wang215326.16
Muquan Zou300.34