Evaluating the Effect of Multimodality in Data on Statistical Clustering and Density Estimation Techniques

Posted: Jul 26, 2022

• Isaac Long
• Isla Hayes
• Jasmine Reed

Abstract

The increasing complexity of modern datasets presents significant challenges for statistical learning techniques, particularly in the context of multimodality in data distributions. Multimodality, characterized by the presence of multiple peaks or modes in probability distributions, represents a fundamental property of many real-world datasets across scientific domains. This research addresses the gap in understanding how different aspects of multimodality systematically affect clustering and density estimation techniques by developing a comprehensive framework for evaluating algorithm performance across a spectrum of multimodality characteristics. The novelty of our approach lies in the systematic decomposition of multimodality into distinct dimensions: the number of modes, their relative separation, asymmetry in mode characteristics, and heterogeneity across data dimensions. This paper makes three primary contributions: first, we introduce a novel framework for generating and characterizing multimodal datasets with precise control over multimodality parameters; second, we provide extensive empirical evaluation of algorithm performance across diverse multimodality scenarios.