This paper proposes a spatial clustering model based on self-organizing feature map and a composite distance measure, and studies the knowledge discovery from spatial database of spatial objects with non-spatial attributes. This paper gives the structure, algorithm of the spatial clustering model based on SOFM. We put forward a composite clustering statistic, which is calculated by both geograp...

Spatial clustering is an important topic in knowledge discovery research. However, most clustering methods do not consider semantic information during the clustering process. In this paper, we present ONTO_CLUST, a framework for ontology-based spatial clustering. Using the framework, spatial clustering can be conducted with the support of a spatial clustering ontology. As an illustration, the f...

Clustering analysis has received considerable attention in spatial data mining for several years. With the rapid development of the geospatial information technologies, the size of spatial information data is growing exponentially which makes clustering massive spatial data a challenging task. In order to improve the efficiency of spatial clustering for large scale data, many researchers propos...

Most test statistics for detecting spatial clustering cannot distinguish between low-value spatial clustering and high-value spatial clustering, and none is designed to explicitly detect high-value clustering, low-value clustering, or both. To fill this void in practice, we introduce an adjustment procedure that can supplement common twosided spatial clustering tests so that a one-sided conclus...

Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human’s ability to analyze. Recently, clusterin...