Fuzzy c-means with regularization by K–L information (KLFCM) is an objective function method for clustering, which is regarded as a fuzzy counterpart of Gaussian mixture models (GMMs) with EM algorithm when the regularization parameter λ equals 2. However, KLFCM method extracts very close or even coincident clusters in many cases because the K–L information term in its objective function is used to minimize the dissimilarity between the membership degrees and the proportions of data belonging to the clusters. To deal with this problem, we propose a new model called fuzzy c-means clustering with conditional probability based K–L information regularization (CKLFCM) which incorporates the conditional probability distributions and the probabilistic dissimilarity functional into the conventional KLFCM algorithm in order to assign appropriate membership degrees to each data point. CKLFCM technique does not suffer from obtaining the unexpected close or coincident clusters. Several experiments are presented to show the effectiveness of the proposed algorithm.

通过 K-L 信息正则化的模糊 c 均值 (KLFCM) 是一种用于聚类的目标函数方法，当正则化参数λ等于 2。但是，KLFCM 方法在很多情况下提取非常接近甚至重合的集群，因为其目标函数中的 K-L 信息项用于最小化隶属度和属于集群的数据比例之间的差异。为了解决这个问题，我们提出了一种新的模型，称为基于 K-L 信息正则化的条件概率的模糊 c 均值聚类（CKLFCM），它将条件概率分布和概率相异函数合并到传统的 KLFCM 算法中，以便分配适当的每个数据点的隶属度。CKLFCM 技术不会受到意外的接近或重合集群的影响。提出了几个实验来证明所提出算法的有效性。