Clustering of Incomplete Data via a Bipartite Graph Structure

TL;DR

This paper introduces a bipartite graph clustering method capable of handling incomplete and heavy-tailed data without needing additional center node information, validated on real financial datasets.

Contribution

The proposed method uniquely infers clusters from incomplete data and models heavy-tailed distributions, overcoming limitations of existing bipartite graph approaches.

Findings

Effective clustering of financial data with heavy tails

Handles incomplete data without center node information

Validated through numerical experiments

Abstract

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.