Abstract
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.
Highlights
A spatial co-location pattern represents a group of spatial objects whose instances are frequently located in a spatial neighborhood
We propose two new types of co-location pattern mining for fuzzy objects, i.e., single co-location pattern mining (SCP) mining and range co-location pattern mining (RCP) mining
Because the basic SCP mining algorithm requires a lot of distance calculation and join operations on fuzzy objects, so we should as much as possible to cut off some of the fuzzy objects that cannot exist in any prevalent co-location patterns
Summary
A spatial co-location pattern represents a group of spatial objects whose instances are frequently located in a spatial neighborhood. In some real applications such as biomedical image analysis and geographical information systems (GIS), the data may not satisfy this assumption.[21] In real life, such as “old man”, “tall tree”, etc, the boundary of these objects cannot be identified these objects are known as fuzzy objects. Fuzzy objects have long been studied in GIS community,[23,24] spatial co-location patterns mining still remain uninvestigated. We propose efficient algorithms to answer SCP and RCP, effective heuristic rules, including pruning most disqualifying objects, reducing the number of mining and narrowing the excavation, etc, are developed to improve the efficiency of co-location pattern mining. Proposed algorithms for answering SCP (single co-location patterns) and RCP (range co-location patterns) mining are presented in Sections 4 and 5.
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