Search

A co-location pattern is a set of spatial features that are strongly correlated in space. However, some of these patterns could be neglected if the prevalence metrics are based solely on the clique (or star) relationship. Hence, the l -reachability co-location pattern is proposed by introducing the l -reachability clique where the members of each instance pair can be reachable to each other in a given step length l. Because the average size of l -reachability co-location patterns tends to be longer, maximal l -reachability co-location pattern mining is researched in this paper. First, some sparsification strategies are introduced to shorten star neighborhood lists of instances in an updated graph called the l -reachability neighbor relationship graph, and then, they are grouped by their corresponding patterns. Second, candidate maximal l -reachability co-location patterns are iteratively detected in a size-independent way on bi-graphs that contain group keys and their intersection sets. Third, the prevalence of each candidate maximal l -reachability co-location pattern is checked in a binary search way with a natural l -reachability clique called the ⌊ l / 2 ⌋ -reachability neighborhood list. Finally, the effectiveness and efficiency of our model and algorithms are analyzed by extensive comparison experiments on synthetic and real-world spatial data sets. [ABSTRACT FROM AUTHOR]

A co-location pattern is a set of spatial features whose instances are frequently correlated to each other in space. Its mining models always consist of two essential steps. One step is to generate neighbor relationships between spatial instances, and another step is to check the prevalence of candidate patterns on the clique, star or Delaunay triangulation relationships. At least three major issues are addressed in this paper. First, since different spatial regions, different distribution densities, it is difficult to set appropriate parameters to generate ideal neighbor relationships. Second, the clique relationship and the others are so strongly rigid that the users' personal interests are suppressed; some interesting patterns are neglected without increasing redundancy. Third, the different strength of correlations among instances are neglected in prevalence calculation. It causes correlations among features to be undifferentiated. Accordingly, the main work of this paper includes: (1) The neighbor relationship generation can be improved on the idea that the distances between an instance and any of its neighbors are not remarkably different. (2) The type- β co-location pattern is defined and checked based on a co-occurrence where the closeness centrality of each instance is not less than a given threshold β. (3) Since the closeness centrality carries strength of correlations among instances, the strength of the correlations between a feature and the other ones in a type- β co-location pattern can be evaluated with prevalence calculation. Finally, experiments on synthetic and real-world spatial data sets are used to assess the effectiveness and efficiency of our works. The results show that fewer spatial neighbor relationships are generated, and more interesting patterns can be discovered by flexibly adjusting β according to the user's preferences. [ABSTRACT FROM AUTHOR]

A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region. The spatial co-location pattern mining problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem for fuzzy objects. Fuzzy objects play an important role in many areas, such as the geographical information system and the biomedical image database. In this paper, we propose two new kinds of co-location pattern mining for fuzzy objects, single co-location pattern mining (SCP) and range co-location pattern mining (RCP), to mining co-location patterns at a membership threshold or within a membership range. For efficient SCP mining, we optimize the basic mining algorithm to accelerate the co-location pattern generation. To improve the performance of RCP mining, effective pruning strategies are developed to significantly reduce the search space. The efficiency of our proposed algorithms as well as the optimization techniques are verified with an extensive set of experiments. [ABSTRACT FROM AUTHOR]