Implementation of Feature Selection to Reduce the Number of Features in Determining the Initial Centroid of K-Means Algorithm

Prasetyo, Vincentius Riandaru and Miranti, Fania Alya and Limanto, Susana (2023) Implementation of Feature Selection to Reduce the Number of Features in Determining the Initial Centroid of K-Means Algorithm. In: International Conference On Informatics, Electrical, And Electronics (ICIEE 2022), 05-07 Oktober 2022, Online Yogyakarta.

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Abstract

Clustering is a data mining method to group data based on its features or attributes. One reasonably popular clustering algorithm is K-Means. K-Means algorithm is often optimized with methods such as the genetic algorithm (GA) to overcome the problem of determining the initial random centroid. Many features in a dataset can reduce the accuracy and increase the computational time of model execution. Feature selection is an algorithm that can reduce data dimension by removing less relevant features for modeling. Therefore, this research will implement Feature selection on the K-Means algorithm optimized with the Dynamic Artificial Chromosome Genetic Algorithm (DAC GA). From the experimental results with ten datasets, it is found that reducing the number of features with feature selection can speed up the computation time of DAC GA to K-Means process by 17,5%. However, all experiments resulted in higher Sum of Square Distance (SSD) and Davies Bouldin Index (DBI) values in clustering results with selected features.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	K-Means, Dynamic Artificial Chromosomes Genetic Algorithm, Feature Selection
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	VINCENTIUS RIANDARU PRASETYO
Date Deposited:	22 Aug 2022 05:28
Last Modified:	10 Feb 2023 08:32
URI:	http://repository.ubaya.ac.id/id/eprint/42360

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