We propose a novel unsupervised k-means (U-k-means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection. The computational complexity of the proposed U-k-means clustering algorithm is analyzed. Comparisons between the proposed U-k-means and other existing methods are also made. Experimental results and comparisons actually demonstrate these good aspects of the proposed U-k-means clustering algorithm.
