It is developed by D. Guo (2008). Like SKATER, REDCAP starts from building a spanning tree with 4 different ways (single-linkage, average-linkage, complete-linkage and wards-linkage). Then, REDCAP provides 2 different ways (first‐order and full-order constraining) to prune the tree to find clusters. The first-order approach with a minimum spanning tree is exactly the same with SKATER. In GeoDa and pygeoda, the following methods are provided:
- First-order and Single-linkage: In this local approach, clusters are formed by considering only immediate neighbors, and their distance is measured by the shortest distance between any pair of points from each cluster.
- Full-order and Complete-linkage: This method also considers all points in the dataset for clustering and calculates the distance between clusters as the average distance between all pairs of points, one from each cluster.
- Full-order and Average-linkage: This method also considers all points in the dataset for clustering and calculates the distance between clusters as the average distance between all pairs of points, one from each cluster.
- Full-order and Single-linkage: Using a global context, this approach considers all points in the dataset for clustering and measures the distance between clusters as the shortest distance between any pair of points from each cluster.
- Full-order and Wards-linkage: All points in the dataset are considered for clustering, and the distance between clusters is calculated in a way that minimizes the internal variance within each cluster.
The node is based on the package pygeoda and here are related tools and references:
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