Skip to main content

MAT-data: Data Preprocessing for Multiple Aspect Trajectory Data Mining

Project description

MAT-data: Data Preprocessing for Multiple Aspect Trajectory Data Mining [MAT-Tools Framework]


[Publication] [citation.bib] [GitHub] [PyPi]

The present package offers a tool, to support the user in the task of data preprocessing of multiple aspect trajectories, or to generating synthetic datasets. It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods.

Created on Dec, 2023 Copyright (C) 2023, License GPL Version 3 or superior (see LICENSE file)

Main Modules

  • proprocess: Methods for trajectory preprocessing;
  • generator: Methods for trajectory datasets generation.

Installation

Install directly from PyPi repository, or, download from github. (python >= 3.7 required)

    pip install mat-data

Getting Started

On how to use this package, see MAT-data-Tutorial.ipynb (or the HTML MAT-data-Tutorial.html)

Citing

If you use mat-data please cite the following paper (this package is fragmented from automatize realease):

Portela, Tarlis Tortelli; Bogorny, Vania; Bernasconi, Anna; Renso, Chiara. AutoMATise: Multiple Aspect Trajectory Data Mining Tool Library. 2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022, pp. 282-285, doi: 10.1109/MDM55031.2022.00060.

Bibtex:

@inproceedings{Portela2022automatise,
    title={AutoMATise: Multiple Aspect Trajectory Data Mining Tool Library},
    author={Portela, Tarlis Tortelli and Bogorny, Vania and Bernasconi, Anna and Renso, Chiara},
    booktitle = {2022 23rd IEEE International Conference on Mobile Data Management (MDM)},
    volume={},
    number={},
    address = {Online},
    year={2022},
    pages = {282--285},
    doi={10.1109/MDM55031.2022.00060}
}

Collaborate with us

Any contribution is welcome. This is an active project and if you would like to include your code, feel free to fork the project, open an issue and contact us.

Feel free to contribute in any form, such as scientific publications referencing this package, teaching material and workshop videos.

Related packages

This package is part of MAT-Tools Framework for Multiple Aspect Trajectory Data Mining:

  • automatize: automatize for experimental evaluation of MAT classification
  • movelets: movelets for MAT classification methods (based on movelets)
  • mat-data: mat-data is a preprocessing library for MAT data
  • mat-analysis: mat-analysis for MAT classification methods
  • mat-view: mat-view for MAT and movelets visualization, and interpratation tools

Change Log

This is a package under construction, see CHANGELOG.md

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mat-data-0.1b11.tar.gz (1.5 MB view details)

Uploaded Source

Built Distribution

mat_data-0.1b11-py3-none-any.whl (1.4 MB view details)

Uploaded Python 3

File details

Details for the file mat-data-0.1b11.tar.gz.

File metadata

  • Download URL: mat-data-0.1b11.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.12.1

File hashes

Hashes for mat-data-0.1b11.tar.gz
Algorithm Hash digest
SHA256 f5176268393f7d441026d5614cc0b7ae06eab5980544bc6d97a5d7b7af142c90
MD5 c5080ad64fc8db971241f07b4a386861
BLAKE2b-256 a702ea7ae0854447445b64d37302c3160f571047884534fa16b6590c0a37f6a2

See more details on using hashes here.

File details

Details for the file mat_data-0.1b11-py3-none-any.whl.

File metadata

  • Download URL: mat_data-0.1b11-py3-none-any.whl
  • Upload date:
  • Size: 1.4 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.12.1

File hashes

Hashes for mat_data-0.1b11-py3-none-any.whl
Algorithm Hash digest
SHA256 8d0fee69eed61f366411683297c4d8a5f07694b6ded17d86552f0b8d6532dd4d
MD5 d2b3d8ca8187d0ad914cdd40db2d99f3
BLAKE2b-256 4088bcaffba7b00a2be78de328b4ac501c8bef69d461baa58f8140b48bd88f81

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page