Chan, H.K.-H. orcid.org/0000-0002-5312-6083, Long, C. orcid.org/0000-0001-6806-8405, Yan, D. orcid.org/0000-0002-4653-0408 et al. (2 more authors) (2024) Fraction-score: a generalized support measure for weighted and maximal co-location pattern mining. IEEE Transactions on Knowledge and Data Engineering, 36 (4). pp. 1582-1596. ISSN 1041-4347
Abstract
Co-location patterns, which capture the phenomenon that objects with certain labels are often located in close geographic proximity, are defined based on a support measure which quantifies the prevalence of a pattern candidate in the form of a label set. Existing support measures share the idea of counting the number of instances of a given label set C as its support, where an instance of C is an object set whose objects collectively carry all labels in C and are located close to one another. However, they suffer from various weaknesses, e.g., fail to capture all possible instances, or overlook the cases when multiple instances overlap. In this paper, we propose a new measure called Fraction-Score which counts instances fractionally if they overlap. Fraction-Score captures all possible instances, and handles the cases where instances overlap appropriately (so that the supports defined are more meaningful and anti-monotonic). We develop efficient algorithms to solve the co-location pattern mining problem defined with Fraction-Score. Furthermore, to obtain representative patterns, we develop an efficient algorithm for mining the maximal co-location patterns, which are those patterns without proper superset patterns. We conduct extensive experiments using real and synthetic datasets, which verified the superiority of our proposals.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Knowledge and Data Engineering is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Clinical Research; Spatial databases; Particle measurements; Itemsets; data mining; Atmospheric measurements; Weight measurement; Computer science |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Oct 2023 08:25 |
Last Modified: | 05 Oct 2024 09:25 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tkde.2023.3304365 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204591 |