Clustering Change Sign Detection by Fusing Mixture Complexity
Kento Urano Ryo Yuki Kenji Yamanishi
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.