LINSCAN -- A Linearity Based Clustering Algorithm

[2406.17952] LINSCAN -- A Linearity Based Clustering Algorithm Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2406.17952 (cs) [Submitted on 25 Jun 2024 ( v1 ), last revised 25 Nov 2025 (this version, v2)] Title: LINSCAN -- A Linearity Based Clustering Algorithm Authors: Andrew Dennehy , Xiaoyu Zou , Shabnam J. Semnani , Yuri Fialko , Alexander Cloninger View a PDF of the paper titled LINSCAN -- A Linearity Based Clustering Algorithm, by Andrew Dennehy and 4 other authors View PDF HTML (experimental) Abstract: DBSCAN and OPTICS are powerful algorithms for identifying clusters of points in domains where few assumptions can be made about the structure of the data. In this paper, we leverage these strengths and introduce a new algorithm, LINSCAN, designed to seek lineated clusters that are difficult to find and isolate with existing methods. In particular, by embedding points as normal distributions approximating their local neighborhoods and leveraging a distance function derived from the Kullback Leibler Divergence, LINSCAN can detect and distinguish lineated clusters that are spatially close but have orthogonal covariances. We demonstrate how LINSCAN can be applied to seismic data to identify active faults, including intersecting faults, and determine their orientation. Finally, we discuss the properties a generalization of DBSCAN and OPTICS must have in order to retain the stability benefits of these algorithms. Subjects: Machine Learning (cs.LG) ; Computational Geometry (cs.CG) Cite as: arXiv:2406.17952 [cs.LG] (or arXiv:2406.17952v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2406.17952 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proc. Mach. Learn. Res. 321:269-286 (2026) Submission history From: Andrew Dennehy [ view email ] [v1] Tue, 25 Jun 2024 21:58:37 UTC (4,243 KB) [v2] Tue, 25 Nov 2025 03:05:06 UTC (665 KB) Full-text links: Access Paper: View a PDF of the paper titled LINSCAN -- A Linearity Based Clustering Algorithm, by Andrew Dennehy and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-06 Change to browse by: cs cs.CG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• A tutorial on spectral clustering#6515
• Federated parameter-free DBSCAN clustering and its application in image recognition.#430702
• Federated parameter-free DBSCAN clustering and its application in image recognition#428192
• A Spatiotemporal Extension of the Neuromorphic DBSCAN Implementation#1123723
• Improving clustering quality evaluation in noisy Gaussian mixtures#144258