Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

540
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
540
Classification of Signals01:30

Classification of Signals

1.0K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.0K
Time-Series Graph00:54

Time-Series Graph

4.7K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.7K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

441
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
441

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion.

Sensors (Basel, Switzerland)·2023
Same author

A Short-Term Hybrid TCN-GRU Prediction Model of Bike-Sharing Demand Based on Travel Characteristics Mining.

Entropy (Basel, Switzerland)·2022
Same author

Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets.

Entropy (Basel, Switzerland)·2021
Same author

A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining.

Healthcare (Basel, Switzerland)·2021
Same author

Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost.

Diagnostics (Basel, Switzerland)·2021
Same author

A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering.

Entropy (Basel, Switzerland)·2020
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 30, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.1K

Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns.

Shenghan Zhou1, Houxiang Liu1, Bang Chen1

  • 1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.

Entropy (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces status set sequential pattern mining with time windows (SSPMTW) to uncover local patterns and periodicity often missed by traditional methods. The new approach enhances pattern discovery and reduces system entropy.

Keywords:
TW-Apriori algorithmdata miningperiodicity analysisstatus set sequential pattern miningtime window

More Related Videos

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.1K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.2K

Related Experiment Videos

Last Updated: Oct 30, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.1K
Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.1K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.2K

Area of Science:

  • Data Mining
  • Pattern Recognition
  • Time Series Analysis

Background:

  • Traditional sequential pattern mining overlooks patterns in local time windows and periodicity.
  • Existing methods fail to capture dynamic rule evolution over time.

Purpose of the Study:

  • To propose a novel method, status set sequential pattern mining with time windows (SSPMTW), for discovering local sequential patterns and their periodicity.
  • To address the limitations of traditional sequential pattern mining by incorporating item status and time window constraints.

Main Methods:

  • Developed the SSPMTW method, considering item status and constraints like time windows, minimum confidence, and coverage.
  • Improved the Apriori algorithm to create the TW-Apriori algorithm for efficient mining.
  • Validated the method and algorithm using small-scale and large-scale examples, analyzing constraint impacts.

Main Results:

  • SSPMTW successfully identifies valuable rules within local time windows and analyzes their periodicity.
  • The TW-Apriori algorithm demonstrates feasibility, validity, and efficiency in large-scale data.
  • Mined rules using SSPMTW lead to a reduction in system entropy.

Conclusions:

  • SSPMTW overcomes limitations of traditional methods by capturing local patterns and periodicity.
  • The proposed method enhances pattern discovery and provides deeper insights into data dynamics.
  • SSPMTW offers a more comprehensive approach to sequential pattern mining.