EEPIS Repository

Centronit: Initial Centroid Designation Algorithm for K-Means Clustering

Barakbah, Ali Ridho and Arai, Kohei (2014) Centronit: Initial Centroid Designation Algorithm for K-Means Clustering. EMITTER International Journal of Engineering Technology, 02 (01). pp. 50-62. ISSN 2355-391X

[img] PDF (EMITTER 2014 - 1) - Published Version
Restricted to Registered users only
Available under License Creative Commons Attribution No Derivatives.

Download (247Kb)


    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 centroid optimization 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.

    Item Type: Article
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
    Depositing User: Dr. Ali Ridho Barakbah
    Date Deposited: 22 Mar 2015 12:16
    Last Modified: 22 Mar 2015 12:16

    Actions (login required)

    View Item