Abstract——Looking for the spatial co-location that appears
frequently in nearby space is widely used in many areas,
including mobile phone services and traffic management. To
achieve this goal, the SGCT algorithm improves other
algorithms which use tables to discover candidate sets. It uses
an undirected graph to mine candidates of the maximal
co-location patterns first, then uses a condensed-tree structure
to store instance cliques of candidates. However, as the amount
of data grows, the SGCT algorithm may store large number of
nodes in the process of generating the tree. In this paper, we
propose a new strategy which will consider the number of
instances of each event. We propose a Count-Ordered
Instances-tree to record candidates of relation sets. From our
experimental results, we show that our approach needs shorter
time and costs less storage space than the SGCT algorithm.
Index Terms—Maximal co-location patterns, spatial
co-location patterns, spatial co-location rules, spatial database,
spatial data mining.
The authors are with National Sun Yat-Sen University, Taiwan (e-mail:
linkc@db.cse.nsysu.edu.tw).
Cite: Ye-In Chang, Wen-Hsiu Chung, and Kuan-Chieh Lin, "Mining Maximal Co-location Patterns Based on the Count-Ordered Instances-Tree in Spatial Databases," International Journal of Machine Learning and Computing vol. 12, no. 1, pp. 23-30, 2022.
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