Learning to cluster urban areas: two competitive approaches and an empirical validation

Keyphrases

• Attributed Graph 50%
• Clustering Algorithm 50%
• Competitive Approach 100%
• Descriptive Characteristics 50%
• Gaussian Mixture Model 100%
• Geographic Unit 50%
• Granularity 50%
• Learning to Cluster 100%
• Modularity 100%
• Parametric Sensitivity 50%
• Santiago De Chile 50%
• Social Features 50%
• Spatial Resolution 50%
• Spatially Distributed Data 50%
• Urban Areas 100%
• Urban Clustering 100%
• Urban Planning 50%

INIS

• algorithms 50%
• applications 50%
• chile 50%
• cities 50%
• data 50%
• graphs 50%
• learning 100%
• mixtures 100%
• planning 50%
• sensitivity 50%
• solutions 50%
• spatial resolution 50%
• surveys 50%
• units 50%
• urban areas 100%
• validation 100%

Computer Science

• Clustering Algorithm 50%
• Descriptive Attribute 50%
• Gaussian Mixture Model 100%
• Granularity 50%
• Social Feature 50%
• Spatial Resolution 50%