Mining Maximal Co-located Event Sets

Yoo, Jin Soung; Bow, Mark

doi:10.1007/978-3-642-20841-6_29

Cited by 23 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CPs mining method was implemented separately for the cases of the entire city and the four sub-regions. Empirically, the Min-prev is set between 0.1 and 0.5 (Leibovici et al., 2014; Yao et al., 2016; Yoo and Bow, 2011). High values of Min-prev may yield too few CPs, while low Min-prev values may increase computation time and generate too many results of CPs.…”

Section: Implementation and Resultsmentioning

confidence: 99%

Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China

Liu

Chen

et al. 2021

Environment and Planning B: Urban Analytics and City Science

View full text Add to dashboard Cite

Industry cluster development is important to stimulate regional economy. Conventional spatial methods for detecting industry clusters use a pairwise manner to infer the co-location relationships of multiple industrial types or instances, which increases the difficulty of interpreting the results. This study proposes to use co-location patterns (CPs) mining method to directly capture the co-location of multiple industrial types from the bottom up without any conditions of data relations defined a priori. The method is applied in Dongguan, China, to investigate the industry cluster patterns at an intra-urban scale. At the city level, the results show prevalent CPs of information communication and technology industry and other associated sectors. At the sub-regional level, however, approximately 41% of the industrial CPs are different from those obtained at the city level. The local features of sub-regional industry clusters are associated with productions of, for instance, sporting goods and toys, digital instrument and office equipment, machine parts and woodware, and textile-related products.

show abstract

Section: Implementation and Resultsmentioning

confidence: 99%

Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China

Liu

Chen

et al. 2021

Environment and Planning B: Urban Analytics and City Science

View full text Add to dashboard Cite

show abstract

“…In [43], Yoo and Bow studied the closed top-k co-location pattern mining problem. The authors also studied the maximal co-location pattern mining problem [42]. In [38], Yao et al proposed to construct a graph based on size-2 co-location patterns, and then find maximal cliques as the maximal co-location pattern candidates for better efficiency.…”

Section: Condensed Co-location Pattern Miningmentioning

confidence: 99%

Fraction-Score: A Generalized Support Measure for Weighted and Maximal Co-Location Pattern Mining

Chan,

Long,

Yan

et al. 2024

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

show abstract

“…Especially, FNR was used to improve the accuracy of the prevalence index calculations in [6]. To reduce the number of prevalent colocations, mining maximal colocation patterns was disposed in [2][3][4]. But as far as we know, no work had been conducted on mining the maximal fuzzy prevalent colocation patterns based on the FNR, which will be addressed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…According to the downward closure property of prevalent colocation patterns, a prevalent colocation pattern is maximal when any of its subsets is prevalent while all of its supersets are not prevalent [2][3][4]. This means that all of the prevalent colocation patterns can be deduced from the maximal prevalent colocation patterns.…”

Section: Introductionmentioning

confidence: 99%

Mining Maximal Fuzzy Colocation Patterns

Wang

Chen

et al. 2022

Modern Management Based on Big Data III

View full text Add to dashboard Cite

Spatial colocation pattern mining is to discover the subsets of spatial objects frequently appearing together in adjacent geographic locations. In the existing research, several algorithms were proposed for excavating maximal prevalent colocation patterns. Furthermore, fuzzy neighborhood relationship(FNR) was employed to evaluate the proximity between spatial instances for improving the accuracy of the mining results. However, the approach for discovering the maximal prevalent colocation patterns based on FNR is not studied yet. This paper defines the maximal fuzzy prevalent colocation pattern (MFPCP). We propose a maximal fuzzy prevalent colocation pattern mining algorithm to generate the MFPCPs instead of all of the prevalent colocation patterns. We conduct experiments on the real datasets to evaluate the performance of the proposed algorithm.

show abstract

Mining Maximal Co-located Event Sets

Cited by 23 publications

References 19 publications

Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China

Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China

Fraction-Score: A Generalized Support Measure for Weighted and Maximal Co-Location Pattern Mining

Mining Maximal Fuzzy Colocation Patterns

Contact Info

Product

Resources

About