Clustering is a crucial step in scientific data analysis and engineering systems. Thus, an efficient cluster analysis method often remains a key challenge. In this paper, we introduce a general purpose exemplar-based clustering method called (MEGA), which performs a novel message-passing strategy based on variational expectation–maximization and generalized arc-consistency techniques. Unlike message passing clustering methods, MEGA formulates the message-passing schema as E- and M-steps of variational expectation–maximization based on a reparameterized factor graph. It also exploits an adaptive variant of generalized arc consistency technique to perform a variational mean-field approximation in E-step to minimize a Kullback–Leibler divergence on the model evidence. Dissimilar to density-based clustering methods, MEGA has no sensitivity to initial parameters. In contrast to partition-based clustering methods, MEGA does not require pre-specifying the number of clusters. We focus on the binary-variable factor graph to model the clustering problem but MEGA is applicable to other graphical models in general. Our experiments on real-world problems demonstrate the efficiency of MEGA over existing prominent clustering algorithms such as Affinity propagation, Agglomerative, DBSCAN, K-means, and EM.

聚类是科学数据分析和工程系统中的关键步骤。因此，有效的聚类分析方法通常仍然是关键挑战。在本文中，我们介绍了一种通用的基于样本的聚类方法（MEGA），该方法基于变分期望最大化和广义弧一致性技术执行一种新颖的消息传递策略。与消息传递聚类方法不同，MEGA根据重新参数化的因子图将消息传递方案表述为变异期望最大化的E和M步骤。它还利用广义弧一致性技术的自适应变体在E步中执行均值场近似，以最小化模型证据上的Kullback-Leibler散度。与基于密度的聚类方法不同，MEGA对初始参数不敏感。与基于分区的群集方法相比，MEGA不需要预先指定群集数量。我们将重点放在二进制变量因子图上，以对聚类问题进行建模，但MEGA通常适用于其他图形模型。我们在现实问题上的实验证明了MEGA在现有的杰出聚类算法（如亲和传播，聚集，DBSCAN，K-means和EM）上的效率。