matclustering.methods.hierarchical.mattree.algorithm namespace

Submodules

matclustering.methods.hierarchical.mattree.algorithm.TreeNodeObject 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.algorithm.TreeNodeObject.TreeNodeObject(df, par=None)

Bases: object

A class used to represent a node in a Tree.

TODO

set_level: Defines the cluster label.

check_label: Method used to verify cluster aspect label in order to avoid

duplicate names in Sankey Diagram.

sankey: Creates a sankey diagram from class dataset. Sankey diagram is a

type of flow diagram in which the width of the arrows is proportional to the flow rate.

eda: Exploratory Data Analysis. It generates a plot bar of a given feature of

a given dataset and a given user.

eda_corr: Exploratory Data Analysis. It generates a plot of correlation

matrix of all features of a given dataset and a given user.

get_similarity_matrix: Creates the distance matrix of the trajectories of

a given cluster using the given similarity metric.

get_entropy: Calculates the entropy value of a given dataset.

dashboard: Displays the result dashboard.

show: Shows info about each cluster node.

graphicTree: Shows info about each cluster node in the tree generated by Digraph

plot.

dashTree: Method that calculates the frequency matrix and the clusters

generated from the division of data based on a split criteria defined in the Tree constructor.

ALL = 'ALL'

HEATMAP = 'HEATMAP'

SELECT = 'Select Node'

absolute_frequency_matrix = None

clusters = 0

dendrogram_dict = {}

df_dict = {}

df_leaves = {}

id_dict = {}

id_iter = count(0)

id_list = []

label = {}

nodeLabel = {}

nodeNum = -1

relative = True

source = []

targetNum = -1

temp = {}

temporario = 0

value = {}

var_dict = {}

matclustering.methods.hierarchical.mattree.algorithm.check_label 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

matclustering.methods.hierarchical.mattree.algorithm.check_label.check_label(self, label, depth)

Method used to verify cluster aspect label in order to avoid duplicate names in Sankey Diagram.

Parameters:

• self

• label (str) – Cluster aspect label.

• depth (int) – Cluster depth level.

matclustering.methods.hierarchical.mattree.algorithm.dashtree 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

matclustering.methods.hierarchical.mattree.algorithm.dashtree.dashtree(self, df, exclude_aspects=None)

Method that calculates the frequency matrix and the clusters generated from the division of data based on a split criteria defined in the Tree constructor.

matclustering.methods.hierarchical.mattree.algorithm.graphic_tree 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

matclustering.methods.hierarchical.mattree.algorithm.graphic_tree.generate_graphic_tree(self, dir_path)

matclustering.methods.hierarchical.mattree.algorithm.graphic_tree.graphic_tree(self, graphTree)

Shows info about each cluster node in the tree generated by Digraph plot.

Parameters:

graphTree (Digraph) – A base class for directed graphs.

matclustering.methods.hierarchical.mattree.algorithm.set_level 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

matclustering.methods.hierarchical.mattree.algorithm.set_level.set_level(id_dict, depth)

Defines the cluster label.

Parameters:

• id_dict

• depth (int) – Tree depth level.