Clustering of Incomplete Data via a Bipartite Graph Structure
This work addresses a practical challenge in data clustering for domains like finance where data is often incomplete and non-Gaussian, offering an incremental improvement over existing bipartite graph methods.
The paper tackles the problem of clustering incomplete data, particularly in financial markets with heavy-tailed distributions, by proposing a bipartite graph model that does not require center node information and demonstrates efficiency in real financial data experiments.
There are various approaches to graph learning for data clustering, incorporating different spectral and structural constraints through diverse graph structures. Some methods rely on bipartite graph models, where nodes are divided into two classes: centers and members. These models typically require access to data for the center nodes in addition to observations from the member nodes. However, such additional data may not always be available in many practical scenarios. Moreover, popular Gaussian models for graph learning have demonstrated limited effectiveness in modeling data with heavy-tailed distributions, which are common in financial markets. In this paper, we propose a clustering method based on a bipartite graph model that addresses these challenges. First, it can infer clusters from incomplete data without requiring information about the center nodes. Second, it is designed to effectively handle heavy-tailed data. Numerical experiments using real financial data validate the efficiency of the proposed method for data clustering.