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A Comparative Study of Frequent Pattern Mining with Trajectory Data.

Shiting Ding1, Zhiheng Li1, Kai Zhang1,2

  • 1Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.

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|October 14, 2022
PubMed
Summary
This summary is machine-generated.

This study compares sequential pattern mining (SPM) algorithms for trajectory data. Contiguous constraint-based SPM algorithms offer concise outputs and balanced performance for trajectory data mining.

Keywords:
data miningsequential pattern miningtraffic congestionvehicle trajectory

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Area of Science:

  • Data Mining
  • Trajectory Data Analysis
  • Sequential Pattern Mining

Background:

  • Trajectory data presents unique challenges for sequential pattern mining (SPM) due to its continuity and uncertainty.
  • Existing research lacks comprehensive comparisons of SPM algorithm performance on trajectory datasets.
  • Data transformation is crucial for preparing trajectory data for SPM.

Purpose of the Study:

  • To evaluate and compare the performance of representative sequential pattern mining algorithms on trajectory data.
  • To understand the impact of various parameters on algorithm performance, including runtime and memory consumption.
  • To guide the selection of optimal SPM algorithms for trajectory data applications.

Main Methods:

  • Selected and applied representative sequential pattern mining algorithms to the T-drive taxi trajectory dataset.
  • Discretized trajectory data for processing with SPM algorithms.
  • Evaluated algorithms based on resultant sequential patterns, runtime, and RAM consumption.
  • Visualized results on Beijing road maps to reflect traffic conditions.

Main Results:

  • Contiguous constraint-based algorithms demonstrated effectiveness in generating concise sequential patterns.
  • These algorithms showed a balance between RAM consumption and execution time, especially at low minimum support (min_sup).
  • Visualizations highlighted traffic congestion patterns derived from the mined sequential patterns.

Conclusions:

  • Contiguous constraint-based SPM algorithms are well-suited for trajectory data analysis.
  • The study provides valuable insights for researchers and practitioners selecting SPM algorithms for trajectory data.
  • Effective data discretization and algorithm choice are key for successful trajectory data mining.