Spatial co-location pattern discovery (SCPD), a kind of knowledge discovery process, aims at discovering potentially unknown co-location patterns (co-locations). Co-locations have been widely used in many aspects, including life services, ecological environment, business research, etc. Many methods have been proposed to discover co-locations. However, these methods only discovered co-locations consisting of ne-grain spatial features, since the user knowledge is ignored, many interesting and general patterns are still undiscovered.Meanwhile, co-locations that are discovered by current frameworks are quantity-numerous and independent, thus, their usefulness is strongly limited. To overcome these shortcomings, this paper introduces the user knowledge into the process of SCPD, to discover general and intrinsic co-locations and help users quickly nd their interested patterns. First, a framework OCPM (Co-location Pattern Miner using Ontology) is proposed, where an ontology is employed to integrate user knowledge to guide the process of SCPD. Second, a new co-location consisting of ontology concepts is proposed. Under the guidance of the ontology, we propose the prevalent semantic multi-level co-locations (PSMCs) consisting of ontology concepts to represent richer knowledge. Third, we design two different ways, i.e., the Apriori-like and clique-based ways, to meet the requirements of OCPM and propose a novel clique-based algorithm named IDG to discover PSMCs. Meanwhile, a top-down search strategy is proposed to help users quickly nd interesting knowledge via the ontology. Finally, we validate OCPM and IDG on both real and synthetic datasets respectively, the experimental results demonstrate their effectiveness. generalized as {mammals, aves}, for a biologist who wants to study the relationship between mammals and aves, both {lions, ostriches} and {mammals, aves} are interesting. However, the latter will not be discovered by current methods.To mine the user's interested patterns, some studies [8, 9,10,11] have been proposed for the post-processing of data mining. However, since many general patterns are still undiscovered, they cannot solve the limitations mentioned above. For discovering richer knowledge, Han et al. proposed an approach for mining multiple-level association rules in transaction databases [12]. They believed that the association rules "chocolate milk → wheat bread" and "milk → bread" both contain important knowledge when the user is concerned with the taste of bread and milk. Their research is effective, however, there is no transaction concept in spatial datasets, thus, their method cannot directly be applied to SCPD. For this reason, they further proposed an approach to get general knowledge by generalizing data in spatial databases [13,14]. The idea of generalization (such as Kamloops is generalized to British Columbia) had inspired researchers to discover multi-level co-location patterns [15, 16]. Based on the Tobler's First Law [17], we can get the generalized concepts of spatial features according ...
