Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm
Dublin Core
Title
Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm
Subject
clustering; initial center; centroid; antlion optimizer; sum of intra-distance clusters; k-means
Description
Clustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, easy to
use and fast in clustering data. The K-means method groups data based on k distances and determines the initial centroid
randomly as a reference for processing. Careless selection of centroids can result in poor clustering processes and local
optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method
for determining the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the
initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better
clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an
increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the Sum
of Intra-Cluster distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli and cancer in each method, the best SICD
value was obtained in the proposed method. Then measuring the best SICD value for each method and datasets is measured
by providing a ranking proving that the proposed method on the iris, wine, cancer datasets gets the first rank and on the ecoli
and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value,
the proposed method is ranked first which provides evidence that the proposed method can improve clustering results and can
be an alternative method for determining the initial center of a cluster using the k-means method.
use and fast in clustering data. The K-means method groups data based on k distances and determines the initial centroid
randomly as a reference for processing. Careless selection of centroids can result in poor clustering processes and local
optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method
for determining the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the
initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better
clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an
increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the Sum
of Intra-Cluster distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli and cancer in each method, the best SICD
value was obtained in the proposed method. Then measuring the best SICD value for each method and datasets is measured
by providing a ranking proving that the proposed method on the iris, wine, cancer datasets gets the first rank and on the ecoli
and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value,
the proposed method is ranked first which provides evidence that the proposed method can improve clustering results and can
be an alternative method for determining the initial center of a cluster using the k-means method.
Creator
Nanang Lestio Wibowo, Moch Arief Soeleman, Ahmad Zainul Fanani
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
August 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Nanang Lestio Wibowo, Moch Arief Soeleman, Ahmad Zainul Fanani, “Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm,” Repository Horizon University Indonesia, accessed February 3, 2026, https://repository.horizon.ac.id/items/show/10057.