Hasil cek similarity_Optimization of Fuzzy C-Means Clustering Algorithm with Combination of Minkowski and Chebyshev Distance Using Principal Component Analysis

Surono, Sugiyarto and Putri, Rizki Desia Arindra Hasil cek similarity_Optimization of Fuzzy C-Means Clustering Algorithm with Combination of Minkowski and Chebyshev Distance Using Principal Component Analysis. [Artikel Dosen]

Text SGY_KIKI_Optimization Of Fuzzy C-Means Clustering Algorithm With Combination Of Minkowski And Chebyshev Distance Using Principal Component Analysis.pdf Download (2MB)

Abstract

Optimization is used to find the maximum or
minimum of a function. In this research, optimization is
applied to the objective function of the FCM algorithm.
FCM is an effective algorithm for grouping data, but it is
often trapped in local optimum solutions. Therefore, the
similarity measure in the clustering process using FCM is
very important. This study uses a new method, which
combines the Minkowski distance with the Chebyshev
distance which is used as a measure of similarity in the
clustering process on FCM. The amount of data that is
quite large and complex becomes one of the difficulties in
providing analysis of multivariate data. To overcome this,
one of the techniques used is dimensional reduction using
Principal Component Analysis (PCA). PCA is an algorithm
of the dimensional reduction method based on the main
components obtained from linear combinations, which can
help stabilize cluster analysis measurements. The method
used in this research is dimensional reduction using PCA,
clustering using FCM with a combination of Minkowski
and Chebyshev distances (FCMMC), and clustering evaluation using the Davies Bouldin Index (DBI). The purpose
of this research is to minimize the objective function of
FCM using new distances, namely, the combination of
Minkowski and Chebyshev distances through the assistance of dimensional reduction by PCA. The results
showed that the cluster accuracy of the combined application of the PCA and FCMMC algorithms was 1.6468.
Besides, the minimum value of the combined objective
function of the two methods is also obtained, namely,
0.0373 which is located in the 15th iteration, where this
value is the smallest value of the 100 maximum iterations
set.

Item Type:	Artikel Dosen
Subjects:	Q Science > QA Mathematics
Divisi / Prodi:	Faculty of Applied Science and Technology (Fakultas Sains Dan Teknologi Terapan) > S1-Mathematics (S1-Matematika)
Depositing User:	Dr Sugiyarto Surono
Date Deposited:	10 Dec 2020 06:44
Last Modified:	10 Dec 2020 06:44
URI:	http://eprints.uad.ac.id/id/eprint/21539

Actions (login required)

View Item