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Parallel Co-location Pattern Mining based on Neighbor-Dependency Partition and Column Calculation

Authors:

Hongmei ChenAuthors Info & Claims

SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems

Pages 365 - 374

https://doi.org/10.1145/3474717.3483984

Published: 04 November 2021 Publication History

Abstract

A co-location pattern is a subset of spatial features whose instances are frequently located together in proximate areas. Mining co-location patterns can discover spatial dependencies in spatial datasets and have particular value in many applications. However, it is challengeable to discover co-location patterns from massive spatial datasets, due to the expensive computational cost. In this paper, we present a novel parallel co-location pattern mining approach. First, dividing spatial neighbor relationships into some neighbor-dependency partitions enables to perform mining task on each partition independently in parallel. Then, a column-based calculation approach is proposed to replace the time-consuming generation of table instances for calculating the prevalence of patterns. To further reduce the search space of patterns on each partition, two pruning strategies are suggested. We implement the parallel co-location pattern mining algorithm based on neighbor-dependency partition and column calculation via MapReduce, named PCPM-NDPCC. Substantial experiments are conducted on real and synthetic datasets to examine the performance of PCPM-NDPCC. Experimental results reveal that PCPM-NDPCC has a significant improvement in efficiency than baseline algorithms and shows better scalability for massive spatial data processing.

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Cited By

Li JWang LChen HSun Z(2025)dGridED-SCPM: A grid-clique-based approach for efficiently mining spatial co-location patternsExpert Systems with Applications10.1016/j.eswa.2024.125471261(125471)Online publication date: Feb-2025
https://doi.org/10.1016/j.eswa.2024.125471
Yang PWang LZhou LChen H(2024)Mining Spatial Co-Location Patterns With a Mixed Prevalence MeasureIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322111235:6(7845-7859)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3221112
Wu PWang LYang PHu X(2024)Representative co-location pattern post-mining based on maximal row instances representation modelKnowledge-Based Systems10.1016/j.knosys.2024.112237301:COnline publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112237
Show More Cited By

Index Terms

Parallel Co-location Pattern Mining based on Neighbor-Dependency Partition and Column Calculation
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems

November 2021

700 pages

ISBN:9781450386647

DOI:10.1145/3474717

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2021

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Project of Innovative Research Team of Yunnan Province
National Natural Science Foundation of China

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SIGSPATIAL '21

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SIGSPATIAL

SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2 - 5, 2021

Beijing, China

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Li JWang LChen HSun Z(2025)dGridED-SCPM: A grid-clique-based approach for efficiently mining spatial co-location patternsExpert Systems with Applications10.1016/j.eswa.2024.125471261(125471)Online publication date: Feb-2025
https://doi.org/10.1016/j.eswa.2024.125471
Yang PWang LZhou LChen H(2024)Mining Spatial Co-Location Patterns With a Mixed Prevalence MeasureIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322111235:6(7845-7859)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3221112
Wu PWang LYang PHu X(2024)Representative co-location pattern post-mining based on maximal row instances representation modelKnowledge-Based Systems10.1016/j.knosys.2024.112237301:COnline publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112237
Jin PWang XXiong WWang LGao S(2024)Extracting Spatial High Utility Co-location Patterns Based on Fuzzy Feature ClustersBig Data and Social Computing10.1007/978-981-97-5803-6_13(217-236)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5803-6_13
Wang LTran VDo T(2023)A Clique-Querying Mining Framework for Discovering High Utility Co-Location Patterns without Generating CandidatesACM Transactions on Knowledge Discovery from Data10.1145/361737818:1(1-42)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617378
Khurana S(2023)A Containerized Cloud Computing Environments with Heterogeneous Task Co-Location2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)10.1109/SMARTGENCON60755.2023.10442864(1-6)Online publication date: 29-Dec-2023
https://doi.org/10.1109/SMARTGENCON60755.2023.10442864
Hu ZWang LTran VChen H(2022)Efficiently mining spatial co-location patterns utilizing fuzzy grid cliquesInformation Sciences: an International Journal10.1016/j.ins.2022.01.059592:C(361-388)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.01.059

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