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Spatial co-location patterns represent the subsets of Boolean spatial features, and the instances of the pattern are frequently located together in a geographic space. Most existing co-location pattern mining methods mainly focus on whether spatial feature instances are frequently located together. However, that the occurrence of neighbor relationships is in the whole space or local area is not considered...
Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. It is difficult to discover co-location patterns because of the huge amount of space data. A common framework for mining spatial co-location patterns employs a level-wised search method to discover co-location patterns, and generates numerous redundant patterns which...
Spatial co-locations represent the subsets of spatial features which are frequently located together in a geographic space. Discovering co-locations has many useful applications. For example, co-located plant species discovered from plant distribution datasets can contribute to the analysis of plant geography, phytosociology studies, and plant protection recommendations. This paper focuses on incremental...
A multimode network involves more than one type of entities. In a multimode network, besides nodes with the same type can form groups, nodes in different groups with the same type or the different types also tend to form bigger groups because nodes in these different groups frequently are related. These bigger groups (named as Associations-between-Groups, for short AGs) provide more information which...
With the explosive growth and extensive applications of spatial data sets, it is becoming more and more important to solve the problem how to discover knowledge automatically from spatial data sets. Co-location patterns discovery is an important branch in spatial data mining. Traditional algorithms for co-location patterns mining can only find positive co-location patterns. However, negative co-location...
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