Enhanced Initial Centroids for K-means Algorithm

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Aleta C. Fabregas 1,* Bobby D. Gerardo 2 Bartolome T. Tanguilig III 3

1. Graduate Programs, Technological Institute of the Philippines, Quezon City, Philippines

2. Institute of Information and Communication Technology, West Visayas State University, Lapaz, Iloilo City, Philippines

3. AMA Head Office, Panay Avenue, Quezon City, Philippines

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2017.01.04

Received: 11 Mar. 2016 / Revised: 6 Jul. 2016 / Accepted: 1 Oct. 2016 / Published: 8 Jan. 2017

Index Terms

K-means algorithm, Euclidian Distance, Centroids, Clustering, Enhanced-K-means algorithm Weighted Average mean


This paper focuses on the enhanced initial centroids for the K-means algorithm. The original k-means is using the random choice of initial seeds which is a major limitation of the original K-means algorithm because it produces less reliable result of clustering the data. The enhanced method of the k-means algorithm includes the computation of the weighted mean to improve the centroids initialization. This paper shows the comparison between K-Means and the enhanced K-Means algorithm, and it proves that the new method of selecting initial seeds is better in terms of mathematical computation and reliability.

Cite This Paper

Aleta C. Fabregas, Bobby D. Gerardo, Bartolome T. Tanguilig III, "Enhanced Initial Centroids for K-means Algorithm", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.1, pp.26-33, 2017. DOI:10.5815/ijitcs.2017.01.04


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