Abstract: The mining of spatial co-location patterns is a crucial research focus within the field of spatial data mining. Its objective is to identify subsets of spatial features where instances frequently coexist in space. However， conventional approaches often rely solely on pattern frequency as a measure of user interest， disregarding subjective user preferences. This results in the extraction of numerous spatial co-location patterns that are uninteresting to users and may impact their subsequent decisions. Therefore， an interactive mining method of spatial co-location patterns combined with user’s interest preference is proposed. Firstly， a new similarity measure index for co-location patterns is introduced by combining Jaccard similarity and semantic similarity. Secondly， personalized clustering based on user feedback is utilized to extract user preferences. Subsequently， the Stochastic Degenerate Tree-Augmented Naive Bayes Integration Model （SDTANI） is proposed to establish a prediction model that integrates both user interest and preference. Finally， an interactive mining algorithm framework is employed to assist users in identifying interesting patterns. Experimental results using synthetic datasets with varying sizes and real datasets demonstrate that the proposed method outperforms other methods in terms of accuracy， particularly with respect to F1-score. Furthermore， it effectively mines spatial co-location patterns based on users’ interests.

Key words: spatial co-location pattern, user interest preferences, interactive mining, personalized clustering, SDTANI