Fuzzy C-Means (FCM) is the most commonly used and discussed fuzzy clustering algorithm in the literature. Nevertheless, it is well known that the performance of FCM is strongly affected by the selection of the initial cluster centers. In other words, the selection of a good set of initial cluster centers plays an important role in the performance of this algorithm. The most common selection method is the trial-and-test random method, in which each execution is performed with different initial centers, randomly generated, resulting in different dataset partitions. This paper proposes two methods to obtain the initial cluster centers which are applied in FCM and its variants. The proposed methods are deterministic, since, for each data set and number of clusters, they will always provide the same cluster centers set. The main advantage of these methods is to provide high quality partitions faster than the original methods as well as other FCM and ckMeans-based algorithms with deterministic selection of cluster centers.

中文翻译：

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基于模糊c均值算法的初始中心的两种确定性选择方法

模糊C均值（FCM）是文献中最常用和讨论的模糊聚类算法。尽管如此，众所周知，FCM的性能会受到初始群集中心的选择的强烈影响。换句话说，选择一组良好的初始聚类中心在该算法的性能中起着重要作用。最常见的选择方法是试验随机方法，其中，每次执行都是使用不同的初始中心随机执行的，从而产生不同的数据集分区。本文提出了两种获取初始聚类中心的方法，该方法适用于FCM及其变体。所提出的方法是确定性的，因为对于每个数据集和聚类数量，它们将始终提供相同的聚类中心集。