Spatial Data Clustering and Pattern Recognition Using Machine Learning

Author(s)	Kirti Vasdev
Country	United States
Abstract	Spatial data clustering and pattern recognition are pivotal in analyzing geospatial information for various applications. With the advent of machine learning (ML), the ability to extract meaningful patterns from complex datasets has been significantly enhanced. This paper explores the integration of ML techniques in clustering and recognizing patterns in spatial data. The discussion includes detailed theories, case studies, and applications in fields such as urban planning, environmental monitoring, and disaster management. Advanced clustering methods like DBSCAN and k-means are evaluated, alongside deep learning-based approaches. Case studies highlight real-world applications, showcasing ML’s role in improving decision-making processes. Challenges, future trends, and opportunities in the domain are also discussed. Diagrams and tables are provided to illustrate methods and results.
Keywords	Spatial data, machine learning, clustering, pattern recognition, geospatial analysis, DBSCAN, k-means, deep learning, urban planning, environmental monitoring
Field	Lainnya
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-01-16
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.23474
Short DOI	https://doi.org/g82hqv

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research