Physical Sciences and Mathematics Commons^™

Optimal Candidate Generation In Spatial Co-Location Mining, Zhongshan Lin

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Existing spatial co-location algorithms based on levels suffer from generating extra, nonclique candidate instances. Thus, they require cliqueness checking at every level. In this thesis, a novel, spatial co-location mining algorithm that automatically generates co-located spatial features without generating any nonclique candidates at any level is proposed. Subsequently, this algorithm generates fewer candidates than other existing level-wise, co-location algorithms without losing any pertinent information. The benefits of this algorithm have been clearly observed at early stages in the mining process.