Statistical Testing Framework for Clustering Pipelines by Selective Inference

TL;DR

This paper introduces a statistical testing framework based on selective inference to evaluate the significance of clustering results in complex data analysis pipelines, ensuring reliable conclusions.

Contribution

It develops a novel, valid statistical testing method for clustering pipelines that controls type I error and is applicable to real-world data analysis workflows.

Findings

The framework controls the type I error rate at any nominal level.

Experiments demonstrate the validity and effectiveness of the proposed tests.

The method is applicable to synthetic and real datasets.

Abstract

A data analysis pipeline is a structured sequence of steps that transforms raw data into meaningful insights by integrating multiple analysis algorithms. In many practical applications, analytical findings are obtained only after data pass through several data-dependent procedures within such pipelines. In this study, we address the problem of quantifying the statistical reliability of results produced by data analysis pipelines. As a proof of concept, we focus on clustering pipelines that identify cluster structures from complex and heterogeneous data through procedures such as outlier detection, feature selection, and clustering. We propose a novel statistical testing framework to assess the significance of clustering results obtained through these pipelines. Our framework, based on selective inference, enables the systematic construction of valid statistical tests for clustering pipelines composed of predefined components. We prove that the proposed test controls the type I error rate at any nominal level and demonstrate its validity and effectiveness through experiments on synthetic and real datasets.