Multiple Sample Clustering

TL;DR

This paper introduces a general framework for multiple sample clustering that handles complex data distributions, demonstrating improved accuracy and stability through specific implementations on synthetic and stock data.

Contribution

It proposes a versatile framework for multiple sample clustering that extends beyond Gaussian assumptions, enabling broader application and improved performance.

Findings

Sufficient statistics significantly enhance clustering accuracy.

Framework applied successfully to synthetic and real stock data.

Wasserstein and Bhattacharyya distance versions outperform traditional methods.

Abstract

The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades. Many clustering algorithms: such as k-means and spectral clustering are proposed based on the single sample assumption. However, in real life, each input object can usually be the multiple samples drawn from a certain hidden distribution. The traditional clustering algorithms cannot handle such a situation. This calls for the multiple sample clustering algorithm. But the traditional multiple sample clustering algorithms can only handle scalar samples or samples from Gaussian distribution. This constrains the application field of multiple sample clustering algorithms. In this paper, we purpose a general framework for multiple sample clustering. Various algorithms can be generated by this framework. We apply two specific cases of this framework: Wasserstein distance version and Bhattacharyya distance version on both synthetic data and stock price data. The simulation results show that the sufficient statistic can greatly improve the clustering accuracy and stability.