Welcome to DeBaCl

DeBaCl is a Python library for estimation of density level set trees and nonparametric density-based clustering. Level set trees are based on the statistically-principled definition of clusters as modes of a probability density function. They are particularly useful for analyzing structure in complex datasets that exhibit multi-scale clustering behavior. DeBaCl is intended to promote the practical use of level set trees through improvements in computational efficiency, flexible algorithms, and an emphasis on modularity and user customizability.

Level Set Tree constructors

construct_tree	Construct a level set tree from tabular data.
construct_tree_from_graph	Construct a level set tree from a similarity graph and a density estimate.
load_tree	Load a saved tree from file.

Level Set Tree model

LevelSetTree	Level Set Tree attributes and methods.
LevelSetTree.prune	Prune the tree by recursively merging small leaf nodes into larger sibling nodes.
LevelSetTree.save	Save a level set tree object to file.
LevelSetTree.plot	Plot the level set tree as a dendrogram and return coordinates and colors of the branches.
LevelSetTree.get_clusters	Generic function for retrieving cluster labels from the level set tree.
LevelSetTree.get_leaf_nodes	Return the indices of leaf nodes in the level set tree.

Utilities

knn_graph	Compute the symmetric k-nearest neighbor graph for a set of points.
epsilon_graph	Construct an epsilon-neighborhood graph, represented by an adjacency list.
knn_density	Compute the kNN density estimate for a set of points.
define_density_mass_grid	Create a grid of density levels with a uniform number of points between each level.
define_density_level_grid	Create an evenly spaced grid of density levels.
reindex_cluster_labels	Re-index integer cluster labels to be consecutive non-negative integers.