Contents 1 Introduction
Competitive learning has been widely applied to a variety of applications such as vector quantization [9], [14], data visualization [8], [13], and particularly to unsupervised clustering [1], [6], [21], [24]. In the literature, k-means [15] is a popular competitive learning algorithm, which trains seed points (also called units hereinafter), denoted as , in a way that they converge to the data cluster centers by minimizing the mean-square-error (MSE) function. In general, k-means algorithm has at least two major drawbacks: 1) It suffers from the dead-unit problem. If the initial positions of some units are far away from the inputs (also called data points interchangeably) in Euclidean space compared to the other units, these distant units will have no opportunity to be trained and, therefore, immediately become dead units. 2) If the number of clusters is misspecified, i.e., is not equal to the true cluster number , the performance of k-means algorithm deteriorates rapidly. Eventually, some of the seed points are not located at the centers of the corresponding clusters. Instead, they are either at some boundary points between different clusters or at points biased from some cluster centers [24].
