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A fast space-saving algorithm for maximal co-location pattern mining
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DOI：10.1016/j.eswa.2016.07.007.png)
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Abstract
En 中文
Real space teems with potential feature patterns with instances that frequently appear in the same locations. As a member of the data-mining family, co-location can effectively find such feature patterns in space. However, given the constant expansion of data, efficiency and storage problems become difficult issues to address. Here, we propose a maximal-framework algorithm based on two improved strategies. First, we adopt a degeneracy-based maximal clique mining method to yield candidate maximal co-locations to achieve high-speed perfotmance. Motivated by graph theory with parameterized complexity, we regard the prevalent size-2 co-locations as a sparse undirected graph and subsequently find all maximal cliques in this graph. Second, we introduce a hierarchical verification approach to construct a condensed instance tree for storing large instance cliques. This strategy further reduces computing and storage complexities. We use both synthetic and real facility data to compare the computational time and storage requirements of our algorithm with those of two other competitive maximal algorithms: order clique -based and MAXColoc. The results show that our algorithm is both more efficient and requires less storage space than the other two algorithms. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Spatial data mining
Maximal co-location patterns
Sparse undirected graph
Condensed tree
Hierarchical verification
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