matsimilarity.methods.mat.LCSS namespace

Submodules

matsimilarity.methods.mat.LCSS.LCSS_T2T module

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

The present package offers a tool, to support the user in the task of data analysis of multiple aspect trajectories. 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 in Dec, 2021 Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)

Authors: - Vanessa Lago Machado - Tarlis Portela

class matsimilarity.methods.mat.LCSS.LCSS_T2T.LCSS(dataset_descriptor: DataDescriptor | None = None)[source]

Bases: SimilarityMeasure

LCSS: Longest Common SubSequence.

This class provides methods to analyze and measure the similarity between multiple aspect trajectory data.

TODO

References

Vlachos, M., Kollios, G., & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. In Data Engineering, 2002. Proceedings. 18th International Conference on (pp. 673-684). IEEE.

similarity(t1: MultipleAspectSequence, t2: MultipleAspectSequence) → float[source]

Compute the similarity between two multiple aspect sequences.

Parameters:

  • t1 (MultipleAspectSequence) – The first multiple aspect trajectory or subtrajectory.

  • t2 (MultipleAspectSequence) – The second multiple aspect trajectory or subtrajectory.

Returns:

The computed similarity score.

Return type:

float