A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm

Understand

K-means is undoubtedly the most widely used partitional clustering algorithm.

• Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers.
• Numerous initialization methods have been proposed to address this problem.
• In this paper, we first present an overview of these methods with an emphasis on their computational efficiency.