Max-p
Last updated
Last updated
The max-p-region problem is a special case of constrained clustering where a finite number of geographical areas are aggregated into the maximum number of regions (max-p-regions), such that each region is geographically connected and the clusters could maximize internal homogeneity.
A simulated annealing algorithm to solve the max-p-region problem
Examples
Try it yourself in the playground (jsgeoda + deck.gl):
A simulated annealing algorithm to solve the max-p-region problem
A simulated annealing algorithm to solve the max-p-region problem
Name
Type
Description
weights
WeightsResult
The weights object WeightsResult
vals
Array
The list of numeric vectors of selected variable.
iterations
Number
The number of iterations of greedy algorithm. Defaults to 1.
min_bounds_values
Array
The list of numeric array of selected minimum bounding variables.
min_bounds
Array
The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
max_bounds_values
Array
The list of numeric array of selected maximum bounding variables.
max_bounds
Array
The list of minimum value that the sum value of bounding variables in each cluster should be less than.
scale_method
String
The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
distance_method
String
The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
seed
Number
The seed for random number generator
Type
Description
ClusteringResult
The Clustering object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}
Name
Type
Description
weights
WeightsResult
The weights object WeightsResult
values
Array
The list of numeric vectors of selected variable.
cooling_rate
Number
The cooling rate of a simulated annealing algorithm. Defaults to 0.85
sa_maxit
Number
The number of iterations of simulated annealing. Defaults to 1
iterations
Number
The number of iterations of greedy algorithm. Defaults to 1.
min_bounds_values
Array
The list of numeric array of selected minimum bounding variables.
min_bounds
Array
The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
max_bounds_values
Array
The list of numeric array of selected maximum bounding variables.
max_bounds
Array
The list of minimum value that the sum value of bounding variables in each cluster should be less than.
scale_method
String
The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
distance_method
String
The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
seed
Number
The seed for random number generator.
Type
Description
ClusteringResult
The Clustering object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}
weights
WeightsResult
The weights object WeightsResult
values
Array
The list of numeric vectors of selected variable.
tabu_length
Number
The length of a tabu search heuristic of tabu algorithm. Defaults to 10.
conv_tabu
Number
The number of non-improving moves. Defaults to 10.
iterations
Number
The number of iterations of greedy algorithm. Defaults to 1.
min_bounds_values
Array
The list of numeric array of selected minimum bounding variables.
min_bounds
Array
The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
max_bounds_values
Array
The list of numeric array of selected maximum bounding variables.
max_bounds
Array
The list of minimum value that the sum value of bounding variables in each cluster should be less than.
scale_method
String
The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
distance_method
String
The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
seed
Number
The seed for random number generator.
Type
Description
ClusteringResult
The Clustering object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}