MODIFIED SELECTION OF INITIAL CENTROIDS FOR K- MEANS ALGORITHM
DOI:
https://doi.org/10.20319/mijst.2016.22.4864Keywords:
K-means Algorithm, Euclidian Distance, Centroids, Clustering, Modified-K-means Algorithm Weighted Average MeanAbstract
This study focuses on the improved initialization of initial centroids instead of random selection for the K-means algorithm. The random selection of initial seeds is a major drawback of the original Kmeans algorithm because it leads to less reliable result of clustering the data. The modified approach of the k-means algorithm integrates the computation of the weighted mean to improve the seeds initialization. This paper shows the comparison of K-Means and Modified K-Means algorithm, using the first simple dataset of four objects and the dataset for service vehicles. The two simple applications proved that the Modified K- Means of selecting initial centroids is more reliable than K-Means Algorithm. Clustering is better achieved in the modified k-means algorithm.
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