POCS-based Clustering Algorithm
This is an incremental improvement for clustering tasks, offering a new method that may benefit data analysis applications.
The paper tackles clustering by proposing a POCS-based algorithm that uses parallel projections to find cluster prototypes, and experimental results show it is competitive and efficient in clustering error and execution speed compared to methods like FCM and K-means.
A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space. The algorithm considers each data point as a convex set and projects the cluster prototypes parallelly to the member data points. The projections are convexly combined to minimize the objective function for data clustering purpose. The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets. The experimental results show that the proposed POCS-based clustering algorithm is competitive and efficient in terms of clustering error and execution speed when compared with other conventional clustering methods including Fuzzy C-Means (FCM) and K-means clustering algorithms.