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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MEDIA KONSERVASI Mechatronics, Electrical Power, and Vehicular Technology Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Applied Engineering and Technology
Kohei Arai
Graduate School of Science and Engineering, Saga University, Saga City, Japan
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Journal : EMITTER International Journal of Engineering Technology

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Centronit: Initial Centroid Designation Algorithm for K-Means Clustering Barakbah, Ali Ridho; Arai, Kohei
EMITTER International Journal of Engineering Technology Vol 2, No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Clustering performance of the K-means highly depends on the correctness of initial centroids. Usually initial centroids for the K- means clustering are determined randomly so that the determined initial centers may cause to reach the nearest local minima, not the global optimum. In this paper, we propose an algorithm, called as Centronit, for designation of initial centroidoptimization of K-means clustering. The proposed algorithm is based on the calculation of the average distance of the nearest data inside region of the minimum distance. The initial centroids can be designated by the lowest average distance of each data. The minimum distance is set by calculating the average distance between the data. This method is also robust from outliers of data. The experimental results show effectiveness of the proposed method to improve the clustering results with the K-means clustering.Keywords: K-means clustering, initial centroids, Kmeansoptimization.
Centronit: Initial Centroid Designation Algorithm for K-Means Clustering Barakbah, Ali Ridho; Arai, Kohei
EMITTER International Journal of Engineering Technology Vol 2, No 1 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v2i1.17

Abstract

Clustering performance of the K-means highly depends on the correctness of initial centroids. Usually initial centroids for the K- means clustering are determined randomly so that the determined initial centers may cause to reach the nearest local minima, not the global optimum. In this paper, we propose an algorithm, called as Centronit, for designation of initial centroidoptimization of K-means clustering. The proposed algorithm is based on the calculation of the average distance of the nearest data inside region of the minimum distance. The initial centroids can be designated by the lowest average distance of each data. The minimum distance is set by calculating the average distance between the data. This method is also robust from outliers of data. The experimental results show effectiveness of the proposed method to improve the clustering results with the K-means clustering.Keywords: K-means clustering, initial centroids, Kmeansoptimization.
Co-Authors Aji Prasetya Wibawa Ali Ridho Barakbah Andrie Asmara, Rosa Anik Nur Handayani Ariyanta, Nadindra Dwi Burhanuddin, Mohd Aboobaider Damayanti, Farradila Ayu Daud, Nathan Dimas Wahyu Wibowo Faqih, Kamil Heru Wahyu Herwanto Hitipeuw, Emanuel Indriati Indriati Jevri Tri Ardiansah Kurnianingtyas, Diva Marji Marji Mustika Mentari Nugraha, Agil Zaidan Priharta, Ari Primadi, Wahyu Rahmat Samudra Anugrah, Muhammad Resty Wulanningrum Rosa Andrie Asmara Sakti Oktafiansyah, Haidar Salsabila, Reni Fatrisna