A partial join approach for mining co-location patterns

Yoo, Jin Soung; Shekhar, Shashi; Smith, John; Kumquat, Julius P.

doi:10.1145/1032222.1032258

Cited by 154 publications

(74 citation statements)

References 16 publications

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“…where a high participation index of a co-location shows that the spatial features of the co-location are more likely to show up together (Yoo and Shekhar 2006).…”

Section: Preliminariesmentioning

confidence: 99%

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A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

2015

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Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting cooccurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus, it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance. Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting co-occurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance.

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“…where a high participation index of a co-location shows that the spatial features of the co-location are more likely to show up together (Yoo and Shekhar 2006).…”

Section: Preliminariesmentioning

confidence: 99%

“…As it is considered, the constrained patterns with PCEs and spatial relationships of objects implicitly (in contrast to existing methods such as Yoo and Shekhar 2006), the proposed methoddefines a spatial co-location pattern by Definition 3 and Equation 2.…”

Section: Basic Conceptsmentioning

confidence: 99%

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

2015

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“…Shekhar et al discussed several interesting approaches to mining co-location patterns, which are subsets of Boolean spatial features whose instances are frequently located together in close proximity [37,47,46]. Huang et al proposed co-location mining involving rare events [17].…”

Section: Spatial Co-location Pattern Discoverymentioning

confidence: 99%

A framework for regional association rule mining and scoping in spatial datasets

et al. 2010

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Advances in database and data acquisition technologies have resulted in an immense amount of spatial data, much of which cannot be readily explored using traditional data analysis techniques. The goal of spatial data mining is to automate the extraction of interesting and useful patterns that are not explicitly represented in spatial datasets. The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically regional, rather than global. Furthermore, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. This raises the challenges on how to measure the interestingness of a set of regions and how to search and evaluate regional patterns among those regions. This paper centers on discovering regional association rules and determining the scope of these rules in spatial datasets. In particular, we present a reward-based region discovery method that employs clustering to find interesting places where regional association rules are valid. A divisive, grid-based supervised clustering algorithm is introduced for region discovery. We evaluate our approach in a real-world case study to identify spatial risk patterns from arsenic in the Texas water supply. Our experimental results not only confirm and validate research results in the study of arsenic contamination, but also lead to the discovery of novel findings that need to be further explored by domain scientists.

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“…Thus, a quick and cheap co-location instance discovery approach is highly desirable. Existing colocation mining algorithms [8,12,17,23,24] largely have their foundation in the generate-and-test strategy of an Apriori algorithm [1]: that is, the algorithms traverse the spatial instances set one level at a time, generate candidates, and then test if each candidate forms a clique. These existing co-location mining approaches are likely to generate and test a huge number of candidates, which creates a bottleneck in the algorithm's performance.…”

Section: Challengesmentioning

confidence: 99%

“…To reduce the number of instance join operations or spatial join operations, a partial-join algorithm was proposed in [23]. This approach is based on plane partitioning.…”

Section: Related Workmentioning

confidence: 99%

Optimal candidate generation in spatial co-location mining

Lin

Lim

2009

Proceedings of the 2009 ACM Symposium on Applied Computing

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Existing spatial co-location algorithms based on levels suffer from generating extra, nonclique candidate instances. Thus, they require cliqueness checking at every level. In this thesis, a novel, spatial co-location mining algorithm that automatically generates co-located spatial features without generating any nonclique candidates at any level is proposed. Subsequently, this algorithm generates fewer candidates than other existing level-wise, co-location algorithms without losing any pertinent information. The benefits of this algorithm have been clearly observed at early stages in the mining process.(77 pages) iv ACKNOWLEDGMENTS

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A partial join approach for mining co-location patterns

Cited by 154 publications

References 16 publications

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

A framework for regional association rule mining and scoping in spatial datasets

Optimal candidate generation in spatial co-location mining

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