Fuzzy co-clustering with automated variable weighting

Charlotte Laclau, Francisco De A.T. De Carvalho, Mohamed Nadif

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose two fuzzy co-clustering algorithms based on the double Kmeans algorithm. Fuzzy approaches are known to require more computation time than hard ones but the fuzziness principle allows a description of uncertainties that often appears in real world applications. The first algorithm proposed, fuzzy double Kmeans (FDK) is a fuzzy version of double Kmeans (DK). The second algorithm, weighted fuzzy double Kmeans (W-FDK), is an extension of FDK with automated variable weighting allowing co-clustering and feature selection simultaneously. We illustrate our contribution using Monte Carlo simulations on datasets with different parameters and real datasets commonly used in the co-clustering context.

Original languageEnglish
Title of host publicationFUZZ-IEEE 2015 - IEEE International Conference on Fuzzy Systems
EditorsAdnan Yazici, Nikhil R. Pal, Hisao Ishibuchi, Bulent Tutmez, Chin-Teng Lin, Joao M. C. Sousa, Uzay Kaymak, Trevor Martin
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467374286
DOIs
Publication statusPublished - 25 Nov 2015
Externally publishedYes
EventIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015 - Istanbul, Turkey
Duration: 2 Aug 20155 Aug 2015

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2015-November
ISSN (Print)1098-7584

Conference

ConferenceIEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
Country/TerritoryTurkey
CityIstanbul
Period2/08/155/08/15

Fingerprint

Dive into the research topics of 'Fuzzy co-clustering with automated variable weighting'. Together they form a unique fingerprint.

Cite this