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Article

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Title

Bounding box representation of co-location instances for Chebyshev and Manhattan metrics

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2023

Published in

Data & Knowledge Engineering

Journal year: 2023 | Journal volume: vol. 145

Article type

scientific article

Publication language

english

Keywords
EN
  • Co-location
  • Bounding box
  • Data mining
Abstract

EN Co-location Pattern Mining (CPM) is the task of discovering sets of spatial features (object types) whose instances are frequently located close to each other in space. Popular co-location discovery methods consist of iteratively: (1) generating co-location candidates, (2) determining instances of these candidates and calculating a measure of potential interestingness, and (3) determining the set of co-locations based on that measure. In this paper, we focus on the second step, as it is the most time-consuming element of CPM. We assume that the distance function is either the Chebyshev or the Manhattan metric. We provide an instance identification method that is characterized by a lower complexity than the state-of-the-art approach. In particular, (1) we introduce a new representation of co-location instances based on bounding boxes, (2) we formulate and prove several theorems regarding such a representation that can improve instances identification step, (3) we provide a novel algorithm that uses the above-mentioned theorems, and (4) we analyze its complexity. To verify our approach, we performed a series of experiments using two real-world datasets.

Date of online publication

07.03.2023

Pages (from - to)

102153-1 - 102153-19

DOI

10.1016/j.datak.2023.102153

URL

https://www.sciencedirect.com/science/article/pii/S0169023X23000137?via%3Dihub

Comments

Article Number: 102153

Ministry points / journal

100

Impact Factor

2,5 [List 2022]

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