Efficiently mining co-location rules on interval data

Authors:
Lizhen Wang;Hongmei Chen;Lihong Zhao;Lihua Zhou
Affiliations:
Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

Spatial co-location rules represent subsets of spatial features whose instances are frequently located together. This paper studies co-location rule mining on interval data and achieves the following goals: 1) defining the semantic proximity between instances, getting fuzzy equivalent classes of instances and grouping instances in a fuzzy equivalent class into a semantic proximity neighborhood, so that the proximity neighborhood on interval data can be rapidly computed and adjusted; 2) defining new related concepts with co-location rules based on the semantic proximity neighborhood; 3) designing an algorithm to mine the above co-location rules efficiently; 4) verifying the efficiency of the method by experiments on synthetic datasets and the plant dataset of "Three Parallel Rivers of Yunnan Protected Areas".