Article Per Year (5 Year)
p-Index From 2019 - 2024
0
P-Index
This Author published in this journals
All Journal Journal of Data Science and Its Applications
Achmad Fauzi Bagus Firmansyah
Politeknik Statistika STIS
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Ensemble Based Gustafson Kessel Fuzzy Clustering Achmad Fauzi Bagus Firmansyah; Setia Pramana
Journal of Data Science and Its Applications Vol 1 No 1 (2018): Journal of Data Science and Its Applications
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/jdsa.2018.1.6

Abstract

Fuzzy clustering is a clustering method whcih allows an object to belong to two or more cluster by combining hard-clustering and fuzzy membership matrix. Two popular algorithms used in fuzzy clustering are Fuzzy C-Means (FCM) and Gustafson Kessel (GK). The FCM use Euclideans distance for determining cluster membership, while GK use Fuzzy Covariance Matrix that considering covariance between variables. Although GK perform better, it has some drawbacks on handling linearly correlated data, and as FCM the algorithm produce unstable result due to random initialization. These drawbacks can be overcame by using improved covariance estimation and cluster ensemble, respectively. This research presents the implementation of improved covariance estimation and cluster ensemble on GK method and compare it with FCM-Ensemble.
Co-Authors Setia Pramana