2018/03/21 by Geoff Boeing, Boeing, Geoff · 1 citation
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Artificial intelligence #Cluster analysis #Computer science #Data Management and Algorithms #Data Mining Algorithms and Applications #Data mining #Data set #FOS: Computer and information sciences #Feature (linguistics) #Geography #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Pattern recognition (psychology) #Remote sensing #Set (abstract data type) #Spatial analysis #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1803.08101
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/03/21 · openalex publication_date 2018/03/21 · arxiv updated 2018/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (plus noise) rather than many distinct spatial features. We use a machine learning approach with density-based clustering to compress such spatial data into a set of representative features.