Introduction to Spatial Data Science with GeoDa
portes grátis
Introduction to Spatial Data Science with GeoDa
Volume 2: Clustering Spatial Data
Anselin, Luc
Taylor & Francis Ltd
05/2024
210
Dura
9781032713021
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
1. Introduction
Part 1: Dimension Reduction
2. Principal Component Analysis (PCA)
3. Multidimensional Scaling (MDS)
4. Stochastic Neighbor Embedding (SNE)
Part 2: Classic Clustering
5. Hierarchical Clustering Methods
6. Partioning Clustering Methods
7. Advanced Clustering Methods
8. Spectral Clustering
Part 3: Spatial Clustering
9. Spatializing Classic Clustering Methods
10. Spatially Constrained Clustering - Hierarchical Methods
11. Spatially Constrained Clustering - Partitioning Methods
Part 4: Assessment
12. Cluster Validation
Part 1: Dimension Reduction
2. Principal Component Analysis (PCA)
3. Multidimensional Scaling (MDS)
4. Stochastic Neighbor Embedding (SNE)
Part 2: Classic Clustering
5. Hierarchical Clustering Methods
6. Partioning Clustering Methods
7. Advanced Clustering Methods
8. Spectral Clustering
Part 3: Spatial Clustering
9. Spatializing Classic Clustering Methods
10. Spatially Constrained Clustering - Hierarchical Methods
11. Spatially Constrained Clustering - Partitioning Methods
Part 4: Assessment
12. Cluster Validation
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
cartography
1. Introduction
Part 1: Dimension Reduction
2. Principal Component Analysis (PCA)
3. Multidimensional Scaling (MDS)
4. Stochastic Neighbor Embedding (SNE)
Part 2: Classic Clustering
5. Hierarchical Clustering Methods
6. Partioning Clustering Methods
7. Advanced Clustering Methods
8. Spectral Clustering
Part 3: Spatial Clustering
9. Spatializing Classic Clustering Methods
10. Spatially Constrained Clustering - Hierarchical Methods
11. Spatially Constrained Clustering - Partitioning Methods
Part 4: Assessment
12. Cluster Validation
Part 1: Dimension Reduction
2. Principal Component Analysis (PCA)
3. Multidimensional Scaling (MDS)
4. Stochastic Neighbor Embedding (SNE)
Part 2: Classic Clustering
5. Hierarchical Clustering Methods
6. Partioning Clustering Methods
7. Advanced Clustering Methods
8. Spectral Clustering
Part 3: Spatial Clustering
9. Spatializing Classic Clustering Methods
10. Spatially Constrained Clustering - Hierarchical Methods
11. Spatially Constrained Clustering - Partitioning Methods
Part 4: Assessment
12. Cluster Validation
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
