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Related Concept Videos

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Cross-Modal Multivariate Pattern Analysis
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Extended vertical lists for temporal pattern mining from multivariate time series.

Anton Kocheturov1, Petar Momcilovic2, Azra Bihorac3

  • 1Center for Applied Optimization, Industrial and Systems Engineering, University of Florida, Gainesville, Florida.

Expert Systems
|November 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces Fast Temporal Pattern Mining with Extended Vertical Lists for complex multivariate time series. The new method significantly speeds up temporal pattern discovery but uses more memory.

Keywords:
frequent pattern mininglevel-wise propertytemporal patternstime-interval patternsvertical data format

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Area of Science:

  • Data Mining
  • Time Series Analysis
  • Pattern Recognition

Background:

  • Mining complex temporal patterns in multivariate time series is challenging.
  • Existing methods may lack efficiency for intricate datasets.

Purpose of the Study:

  • Introduce a novel method for efficient complex temporal pattern mining.
  • Improve upon existing temporal pattern mining algorithms.

Main Methods:

  • Developed Fast Temporal Pattern Mining with Extended Vertical Lists (FTPM-EVL).
  • Utilized Extended Vertical Lists (EVL) data structure.
  • Extended the level-wise property for pattern discovery.

Main Results:

  • FTPM-EVL demonstrates significantly faster performance compared to previous algorithms.
  • The enhanced speed is achieved with a trade-off of increased memory consumption.

Conclusions:

  • FTPM-EVL offers a more efficient approach to mining complex temporal patterns.
  • The method is suitable for large-scale multivariate time series analysis where speed is critical.