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

Rough set based generalized fuzzy c-means algorithm and quantitative indices.

Pradipta Maji1, Sankar K Pal

  • 1Center for Soft Computing Research, Indian Statistical Institute, Kolkata 700 108, India. pmaji@isical.ac.in

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 9, 2008
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

Mental health problems raise the odds of cognitive impairment in COVID-19 survivors.

Frontiers in psychiatry·2024
Same author

Multi-Task Learning and Sparse Discriminant Canonical Correlation Analysis for Identification of Diagnosis-Specific Genotype-Phenotype Association.

IEEE/ACM transactions on computational biology and bioinformatics·2024
Same author

Discriminative Deep Canonical Correlation Analysis for Multi-View Data.

IEEE transactions on neural networks and learning systems·2023
Same author

Multi-View Kernel Learning for Identification of Disease Genes.

IEEE/ACM transactions on computational biology and bioinformatics·2023
Same author

Truncated Normal Mixture Prior Based Deep Latent Model for Color Normalization of Histology Images.

IEEE transactions on medical imaging·2023
Same author

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach.

Environmental monitoring and assessment·2022
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
See all related articles

A new rough-fuzzy possibilistic c-means (RFPCM) algorithm integrates rough and fuzzy set theories for robust unsupervised learning. This hybrid approach enhances clustering by handling uncertainty and overlapping data more effectively than existing methods.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Mining

Background:

  • Unsupervised learning algorithms like fuzzy c-means (FCM) and possibilistic c-means (PCM) face challenges with noise sensitivity and coincident clusters, respectively.
  • Handling uncertainty, vagueness, and incomplete class definitions is crucial for effective data clustering.
  • Existing methods often struggle with overlapping data partitions and noise, limiting their applicability.

Purpose of the Study:

  • To propose a generalized hybrid unsupervised learning algorithm, rough-fuzzy possibilistic c-means (RFPCM).
  • To integrate the principles of rough sets for uncertainty handling and fuzzy sets for overlapping partitions.
  • To develop a robust clustering algorithm that overcomes limitations of existing c-means variants.

Main Methods:

Related Experiment Videos

  • The RFPCM algorithm combines probabilistic and possibilistic memberships simultaneously.
  • It utilizes lower and upper approximations from rough sets to manage uncertainty and incompleteness.
  • A novel concept of crisp lower bound and fuzzy boundary aids in efficient cluster prototype selection.

Main Results:

  • The RFPCM algorithm demonstrates improved performance in handling noisy and overlapping data.
  • Quantitative indices based on rough sets were introduced for algorithm evaluation.
  • The algorithm proved effective on real-life datasets, outperforming other clustering methods.

Conclusions:

  • The proposed RFPCM algorithm offers a generalized framework for c-means variants.
  • It effectively addresses noise sensitivity and coincident cluster issues.
  • RFPCM provides a robust and versatile solution for complex unsupervised learning tasks.