This paper proposes an early detection method for cluster structural changes.
Cluster structure refers to discrete structural characteristics, such as the
number of clusters, when data are represented using finite mixture models, such
as Gaussian mixture models. We focused on scenarios in which the cluster
structure gradually changed over time. For finite mixture models, the concept
of mixture complexity (MC) measures the continuous cluster size by considering
the cluster proportion bias and overlap between clusters. In this paper, we
propose MC fusion as an extension of MC to handle situations in which multiple
mixture numbers are possible in a finite mixture model. By incorporating the
fusion of multiple models, our approach accurately captured the cluster
structure during transitional periods of gradual change. Moreover, we introduce
a method for detecting changes in the cluster structure by examining the
transition of MC fusion. We demonstrate the effectiveness of our method through
empirical analysis using both artificial and real-world datasets.

该论文提出了一种早期检测方法，用于集群结构变化。借助有限混合模型表示数据时，集群结构指离散的结构特征，比如集群数目。我们关注的是集群结构逐渐随时间变化的情况。对于有限混合模型，通过考虑集群比例偏差和集群之间的重叠，混合复杂度（MC）衡量了连续的集群大小。我们提出了 MC 融合作为 MC 的扩展，用于处理有限混合模型中可能存在多个混合数的情况。通过整合多个模型，我们的方法准确地捕捉了渐变期间的集群结构。此外，我们引入了一种通过分析 MC 融合的转变来检测集群结构变化的方法。通过使用人工和真实世界数据集进行实证分析，我们证明了我们方法的有效性。