matclustering.methods.hierarchical.mattree namespace

Subpackages

• matclustering.methods.hierarchical.mattree.algorithm namespace
  • Submodules
  • matclustering.methods.hierarchical.mattree.algorithm.TreeNodeObject module
    • TreeNodeObject
      • TreeNodeObject.TODO
      • TreeNodeObject.ALL
      • TreeNodeObject.HEATMAP
      • TreeNodeObject.SELECT
      • TreeNodeObject.absolute_frequency_matrix
      • TreeNodeObject.clusters
      • TreeNodeObject.dendrogram_dict
      • TreeNodeObject.df_dict
      • TreeNodeObject.df_leaves
      • TreeNodeObject.id_dict
      • TreeNodeObject.id_iter
      • TreeNodeObject.id_list
      • TreeNodeObject.label
      • TreeNodeObject.nodeLabel
      • TreeNodeObject.nodeNum
      • TreeNodeObject.relative
      • TreeNodeObject.source
      • TreeNodeObject.targetNum
      • TreeNodeObject.temp
      • TreeNodeObject.temporario
      • TreeNodeObject.value
      • TreeNodeObject.var_dict
  • matclustering.methods.hierarchical.mattree.algorithm.check_label module
    • check_label()
  • matclustering.methods.hierarchical.mattree.algorithm.dashtree module
    • dashtree()
  • matclustering.methods.hierarchical.mattree.algorithm.graphic_tree module
    • generate_graphic_tree()
    • graphic_tree()
  • matclustering.methods.hierarchical.mattree.algorithm.set_level module
    • set_level()
• matclustering.methods.hierarchical.mattree.metrics_evaluation namespace
  • Submodules
  • matclustering.methods.hierarchical.mattree.metrics_evaluation.eda module
    • eda()
    • eda_corr()
  • matclustering.methods.hierarchical.mattree.metrics_evaluation.entropy module
    • get_entropy()
  • matclustering.methods.hierarchical.mattree.metrics_evaluation.freq_matrix module
    • generate_freq_matrix()
  • matclustering.methods.hierarchical.mattree.metrics_evaluation.sankey module
    • sankey()
  • matclustering.methods.hierarchical.mattree.metrics_evaluation.similarity_matrix module
    • get_similarity_matrix()

Submodules

matclustering.methods.hierarchical.mattree.MATTree module

MAT-Tools: Python Framework for Multiple Aspect Trajectory Data Mining

The present application offers a tool, to support the user in the clustering of multiple aspect trajectory data.It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods. Copyright (C) 2022, MIT license (this portion of code is subject to licensing from source project distribution)

Created on Apr, 2024 Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)

Authors:

• Tarlis Portela

• Yuri Santos

class matclustering.methods.hierarchical.mattree.MATTree.MATTree(exclude_aspects=[], random_state=1, n_jobs=1, verbose=False)

Bases: TrajectoryClustering

MAT-Tree: A Tree-Based Method for Multiple Aspect Trajectory Clustering.

The MATTree class provides an implementation of a tree-based hierarchical clustering method for analyzing trajectory data with multiple aspects.

Parameters:

• exclude_aspects (list, optional) – A list of aspects to exclude from the clustering process. If empty, all aspects are used for clustering. Default is an empty list.

• random_state (int, optional) – Seed for random number generation, ensuring reproducibility. Default is 1 (not used).

• n_jobs (int, optional) – The number of jobs to run in parallel for both fit and predict. Default is 1.

• verbose (bool, optional) – If True, enables verbose output during processing. Default is False.

prepare_input(X, metric=None, dataset_descriptor=None, tid_col='tid', label_col='label'):

Prepares the input data for clustering by extracting relevant trajectory information.

create(config=None):

Initializes and returns a tree-based model for clustering (currently not implemented).

fit(X, config=None):

Fits the model to the trajectory data X, applying the clustering algorithm.

digraph():

Creates a directed graph representation of the clustering tree.

create(config=None)

digraph()

fit(X, config=None)

if_config(config=None)

prepare_input(X, metric=None, dataset_descriptor=None, tid_col='tid', label_col='label')