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

Classification of Systems-II01:31

Classification of Systems-II

537
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
537
Classification of Systems-I01:26

Classification of Systems-I

637
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
637
Aggregates Classification01:29

Aggregates Classification

1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K
Classification of Signals01:30

Classification of Signals

1.5K
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...
1.5K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.6K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K

You might also read

Related Articles

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

Sort by
Same author

Design of a Fano-resonance-enhanced dielectric grating for ultralow-filling-factor superconducting nanowire single-photon detector.

Scientific reports·2026
Same author

Identification of potential biomarkers and therapeutic targets for liver cirrhosis based on Mendelian randomization and machine learning.

Biochemistry and biophysics reports·2026
Same author

[Evaluation of the curative effect of pulpotomy combined with prefabricated transparent crown for traumatic anterior primary tooth].

Shanghai kou qiang yi xue = Shanghai journal of stomatology·2025
Same author

Alzheimer's Disease Risk Prediction and Pathogeny Extraction Using Fuzzy Graph Evolutionary Generative Adversarial Network.

IEEE transactions on neural networks and learning systems·2025
Same author

Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis.

BMJ health & care informatics·2025
Same author

MSAFF: Multi-Way Soft Attention Fusion Framework With the Large Foundation Models for the Diagnosis of Alzheimer's Disease.

IEEE transactions on neural networks and learning systems·2025

Related Experiment Video

Updated: Feb 26, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Hierarchical trie packet classification algorithm based on expectation-maximization clustering.

Xia-An Bi1, Junxia Zhao1

  • 1College of Mathematics and Computer Science, Hunan Normal University, Changsha, P.R. China.

Plos One
|July 14, 2017
PubMed
Summary
This summary is machine-generated.

A new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC), improves network performance. This algorithm effectively handles large rule sets by clustering rules and optimizing trie structures, reducing backtracking and enhancing update efficiency.

Related Experiment Videos

Last Updated: Feb 26, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Area of Science:

  • Computer Science
  • Network Engineering
  • Data Mining

Background:

  • Increasing computer network bandwidth necessitates efficient packet classification for large rule sets.
  • Hierarchical trie algorithms are widely used but suffer from backtracking and empty nodes.
  • Existing packet classification methods require optimization for performance and update efficiency.

Purpose of the Study:

  • To propose a novel packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC).
  • To address the limitations of traditional hierarchical tries, specifically backtracking and update inefficiency.
  • To enhance the performance of packet classification in high-bandwidth networks.

Main Methods:

  • Formalization of packet classification by mapping rules and data packets into a two-dimensional space.
  • Application of the expectation-maximization algorithm to cluster rules based on aggregate characteristics.
  • Development of a hierarchical trie structure optimized using clustering results, incorporating path compression.

Main Results:

  • The proposed HTEMC algorithm effectively clusters rules, forming diversified groups.
  • Hierarchical trie path compression eliminates backtracking, improving classification speed.
  • The algorithm demonstrates significantly improved performance in both simulation and real-environment experiments compared to typical algorithms.
  • Trie update efficiency is enhanced, addressing a key limitation of existing methods.

Conclusions:

  • HTEMC offers a superior approach to packet classification for large-scale rule sets.
  • The integration of expectation-maximization clustering with hierarchical tries provides significant performance gains.
  • This algorithm represents a substantial advancement in network packet classification technology.