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

Information-based clustering.

Noam Slonim1, Gurinder Singh Atwal, Gasper Tkacik

  • 1Joseph Henry Laboratories of Physics, and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA. nslonim@princeton.edu

Proceedings of the National Academy of Sciences of the United States of America
|December 15, 2005
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

Searching for sequence features that control DNA cyclizability.

PNAS nexus·2026
Same author

The FlEye camera: Sampling the joint distribution of natural scenes and motion.

Physical review. E·2026
Same author

Long-term evolution of regulatory DNA sequences. Part 1: simulations on global, biophysically-realistic genotype-phenotype maps.

Current opinion in genetics & development·2026
Same author

Long-term evolution of regulatory DNA sequences. Part 2: theory and future challenges.

Current opinion in genetics & development·2026
Same author

Maximum entropy models for patterns of gene expression.

Physical review. E·2025
Same author

Invariant non-equilibrium dynamics of transcriptional regulation optimize information flow.

ArXiv·2025
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

This study introduces a novel information-theoretic approach to data clustering, overcoming limitations of traditional methods. The new technique yields more coherent clusters by focusing on collective similarity, enhancing data analysis across disciplines.

Area of Science:

  • Data Science
  • Information Theory
  • Computational Statistics

Background:

  • Clustering is vital for analyzing large datasets across various scientific fields.
  • Existing clustering algorithms often rely on restrictive assumptions about data structure.
  • These assumptions can limit the effectiveness and applicability of traditional clustering methods.

Purpose of the Study:

  • To reformulate the clustering problem using an information-theoretic perspective.
  • To develop a clustering method that avoids common assumptions of existing algorithms.
  • To introduce a novel approach for data exploration and analysis.

Main Methods:

  • Developed an information-theoretic framework for clustering.
  • Avoided the need for cluster prototypes and a priori similarity metrics.

Related Experiment Videos

  • Ensured invariance to data representation and captured nonlinear relationships.

    • Main Results:

    • The proposed information-theoretic clustering approach consistently produced more coherent clusters.
    • Demonstrated effectiveness across diverse application domains.
    • Successfully captured nonlinear data relations, a limitation of many existing methods.

    Conclusions:

    • The novel information-theoretic clustering method offers a robust alternative to traditional techniques.
    • This approach enhances data analysis by utilizing collective similarity measures.
    • The method's flexibility and ability to handle complex data structures advance the field of data exploration.