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Abstract: The present paper considers the problem of partitioning a dataset into a known number of clusters using the sum of squared errors criterion (SSE). A new clustering method, called DE-KM, which combines differential evolution algorithm (DE) with the well known K-means procedure is described. In the method, the K-means algorithm is used to fine-tune each candidate solution obtained by mutation and crossover operators of DE. Additionally, a reordering procedure which allows the evolutionary algorithm to tackle the redundant representation problem is proposed. The performance of the DE-KM clustering method is compared to the performance of differential evolution, global K-means method, genetic K-means algorithm and two variants of the K-means algorithm. The experimental results show that if the number of clusters K is sufficiently large, DE-KM obtains solutions with lower SSE values than the other five algorithms. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel eye detection method, which can locate the accurate positions of the eyes from frontal face images. The proposed method is robust to pose changes, different facial expressions and illumination variations. Initially, it utilizes image enhancement, Gabor transformation and cluster analysis to extract eye windows. It then localizes the pupil centers by applying two neighborhood operators within the eye windows. Experiments with the color FERET and the LFW (Labeled Face in the Wild) datasets (including a total of 3587 images) are used to evaluate this method. The experimental results demonstrate the consistent robustness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Threshold selection problem is similar to the clustering problem which can be formulated as one to find the number of clusters and a partition matrix from a given data set using the iterative or non-iterative algorithms. In this paper, a new threshold selection method based on the cluster analysis is proposed. Experimental results are provided to show the effectiveness of the proposed threshold selection method. [Copyright &y& Elsevier]