Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation

This paper introduces the Automated Two-Dimensional K-Means (A2DKM) algorithm, a novel unsupervised clustering technique. The proposed technique differs from the conventional clustering techniques because it eliminates the need for users to determine the number of clusters. In addition, A2DKM incorporates local and spatial information of the data into the clustering analysis. A2DKM is qualitatively and quantitatively compared with the conventional clustering algorithms, namely, the K-Means (KM), Fuzzy C-Means (FCM), Moving K-Means (MKM), and Adaptive Fuzzy K-Means (AFKM) algorithms. The A2DKM outperforms these algorithms by producing more homogeneous segmentation results.

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► An Automated 2D K-Means (A2DKM) clustering algorithm is proposed.
► The proposed A2DKM algorithm eliminates the determination of number of clusters.
► The A2DKM incorporates local and spatial information of data during clustering process.
► Findings indicate the A2DKM outperforms other state-of-the-art clustering techniques.