A Wasserstein distance-based spectral clustering method for transaction data analysis

TL;DR

This paper introduces a novel spectral clustering method based on Wasserstein distance for analyzing merchant transaction data, effectively capturing distributional differences without information loss, and scalable to large datasets.

Contribution

It proposes a Wasserstein-distance-based spectral clustering approach with a subsampling method for large-scale transaction data analysis, improving over traditional feature-based methods.

Findings

Outperforms feature-based methods in identifying merchant behavior patterns

Theoretical properties confirm the efficiency of the approach

Effective on large-scale datasets with limited computational resources

Abstract

With the rapid development of online payment platforms, it is now possible to record massive transaction data. Clustering on transaction data significantly contributes to analyzing merchants' behavior patterns. This enables payment platforms to provide differentiated services or implement risk management strategies. However, traditional methods exploit transactions by generating low-dimensional features, leading to inevitable information loss. In this study, we use the empirical cumulative distribution of transactions to characterize merchants. We adopt Wasserstein distance to measure the dissimilarity between any two merchants and propose the Wasserstein-distance-based spectral clustering (WSC) approach. Based on the similarities between merchants' transaction distributions, a graph of merchants is generated. Thus, we treat the clustering of merchants as a graph-cut problem and solve it under the framework of spectral clustering. To ensure feasibility of the proposed method on large-scale datasets with limited computational resources, we propose a subsampling method for WSC (SubWSC). The associated theoretical properties are investigated to verify the efficiency of the proposed approach. The simulations and empirical study demonstrate that the proposed method outperforms feature-based methods in finding behavior patterns of merchants.