Enhancing K-Means Algorithm with Initial Cluster Centers Derived from Data Partitioning along the Data Axis with PCA
Adnan Ibrahim Alrabea, A. V. Senthilkumar, Hasan Al-Shalabi, Ahmad S. Bader · Journal of Advances in Computer Networks · 2013
Representing the data by smaller amount of clusters necessarily loses certain fine details, but achieves simplification.The most commonly used efficient clustering technique is k-means clustering.The better results of K-Means clustering can be achieved after computing more than one times.In this paper, a new approach is proposed for computing the initial centroids for K-means.This paper uses the first principal component generated using Principal Component Analysis (PCA) for initializing the centroid for K-Means clustering.Initially, the principal components in the dataset are gathered using PCA.From the obtained components, the first principal component is used for initializing the cluster centroid.As a result developed technique helps in decreasing the clustering time at the same time, the clustering accuracy is better for the proposed technique when compared to the existing technique.