Optimal initialization of K-means using Particle Swarm Optimization
This addresses the initialization sensitivity issue in K-means clustering for data analysis applications, but it is incremental as it combines existing methods.
The paper tackles the problem of improving K-means clustering accuracy by using Particle Swarm Optimization (PSO) to determine initial centroids, resulting in enhanced performance as measured by accuracy metrics.
This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data could act as a fitness measure. The resultant optimal vector can be used as the initial centroids for K-means.