matclustering.methods.hierarchical.mattree.metrics_evaluation namespace

Submodules

matclustering.methods.hierarchical.mattree.metrics_evaluation.eda 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.metrics_evaluation.eda.eda(dataset, feature, usr)[source]

Exploratory Data Analysis. It generates a plot bar of a given feature of a given dataset and a given user.

Parameters:

  • usr (int)

  • dataset (pandas.DataFrame) – Dataset of trajectories of a given cluster.

  • feature (str) – Feature or Aspect intended for EDA.

  • usr – User label in a given cluster.

matclustering.methods.hierarchical.mattree.metrics_evaluation.eda.eda_corr(dataset, usr)[source]

Exploratory Data Analysis. It generates a plot of correlation matrix of all features of a given dataset and a given user.

Parameters:

  • dataset (pandas.DataFrame) – Dataset of trajectories of a given cluster.

  • user (int) – User label in a given cluster.

matclustering.methods.hierarchical.mattree.metrics_evaluation.entropy 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.metrics_evaluation.entropy.get_entropy(dataset)[source]

Calculates the entropy value of a given dataset.

Parameters:

dataset (pandas.DataFrame) – Dataset of trajectories of a given cluster

Returns:

Entropy value of a given cluster.

Return type:

Float

matclustering.methods.hierarchical.mattree.metrics_evaluation.freq_matrix 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.metrics_evaluation.freq_matrix.generate_freq_matrix(self, exclude_aspects=None)[source]

Method to generate the frequency matrix

INPUT
exclude_aspects: A list of aspects to exclude for clustering or a empty list to use all aspects.

e.g.: aspects_for_clustering = [] # use all aspects aspects_for_clustering = [‘day’, ‘weather’, ‘root_type’] # exclude aspects for fsny aspects_for_clustering = [‘data’, ‘idVotacao’, ‘parlamentar’] # exclude aspects for basometro

matclustering.methods.hierarchical.mattree.metrics_evaluation.sankey 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.metrics_evaluation.sankey.sankey(cls, label, value)[source]

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.

matclustering.methods.hierarchical.mattree.metrics_evaluation.similarity_matrix 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.metrics_evaluation.similarity_matrix.get_similarity_matrix(cls, dataset, sim_measure)[source]

Creates the distance matrix of the trajectories of a given cluster using the given similarity metric.

Parameters:

  • dataset (pandas.DataFrame) – Dataset of trajectories of a given cluster

  • sim_measure (str) – Similarity metric [MUITAS, MSM, EDR, LCSS].

Returns:

A dataframe of [MUITAS, MSM, EDR, LCSS] similarity metric.

Return type:

pandas.DataFrame