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 Experiment Videos

Dominant sets and pairwise clustering.

Massimiliano Pavan1, Marcello Pelillo

  • 1Dipartimento de Informatica, Università Ca'Foscari de Venezia, Venezia Mestre, Italy. pavan@dsi.unive.it

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 17, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Vector Flows That Compute the Capacity of Discrete Memoryless Channels.

Entropy (Basel, Switzerland)·2025
Same author

The Group Loss++: A Deeper Look Into Group Loss for Deep Metric Learning.

IEEE transactions on pattern analysis and machine intelligence·2022
Same author

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

LiSSA: Localized Stochastic Sensitive Autoencoders.

IEEE transactions on cybernetics·2019
Same author

A Functional Representation for Graph Matching.

IEEE transactions on pattern analysis and machine intelligence·2019
Same author

Dominant Sets for "Constrained" Image Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2018
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study introduces a novel graph theory method for data clustering, defining dominant sets to generalize maximal complete subgraphs. This approach utilizes continuous optimization for efficient clustering in various applications.

Area of Science:

  • Graph theory
  • Data science
  • Computational mathematics

Background:

  • Traditional clustering methods often struggle with complex data structures.
  • The concept of a cluster can be intuitively linked to graph structures.
  • Generalizing existing graph concepts is needed for advanced data analysis.

Purpose of the Study:

  • To develop a novel graph-theoretic approach for pairwise data clustering.
  • To introduce and define the concept of a 'dominant set' in graph theory.
  • To leverage continuous optimization techniques for clustering.

Main Methods:

  • Developing a graph-theoretic framework for data clustering.
  • Defining dominant sets as a generalization of maximal complete subgraphs for edge-weighted graphs.

Related Experiment Videos

  • Establishing a correspondence between dominant sets and quadratic form extrema over a standard simplex.
  • Applying continuous optimization techniques from evolutionary game theory.

    • Main Results:

    • A new graph-theoretic approach for pairwise data clustering has been successfully developed.
    • The concept of dominant sets is introduced and mathematically defined.
    • The approach allows for the use of straightforward and implementable continuous optimization techniques.
    • Numerical examples demonstrate the potential of the approach in point-set and image segmentation problems.

    Conclusions:

    • The proposed graph-theoretic approach offers a promising new method for data clustering.
    • The concept of dominant sets provides a powerful tool for understanding and implementing clustering algorithms.
    • The integration with continuous optimization techniques enhances the efficiency and applicability of the method.