Spatial Data Clustering and Pattern Recognition Using Machine Learning
Kirti Vasdev · International Journal For Multidisciplinary Research · 2024
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.