Using spatial data and cluster analysis to automatically detect non-trivial relationships between environmental transgressors.
José Alberto Sousa TorresPaulo Henrique dos SantosDaniel Alves da SilvaCarlos Eduardo Lacerda VeigaMárcio Bastos MedeirosGuilherme Fay VerqaraFábio Lúcio Lopes de MendonçaRafael Timóteo de Sousa JúniorPublished in: ICDM (Workshops) (2022)
Keyphrases
- cluster analysis
- spatial data
- data analysis
- spatial relationships
- spatial databases
- spatial data mining
- categorical data
- spatial objects
- geospatial data
- geographic information systems
- clustering algorithm
- clustering method
- data mining
- r tree
- geographical information systems
- data mining techniques
- geo spatial
- hierarchical latent class models
- cluster validity
- k means
- spatial queries
- topological relationships
- spatial analysis
- unsupervised learning
- data sets
- latent class models
- spatial database systems
- geographical data
- spatial clustering
- spatial index
- geographical information
- information retrieval
- databases
- real world
- geographic information
- training data
- pattern recognition
- nearest neighbor
- multi dimensional