Hidden Patterns in Clustering

An Exploration of Latent Models with code in R

Image by the author

Abstract

In data analysis, one of the most compelling challenges is the identification and understanding of hidden structures within complex datasets. These structures, often not immediately observable, hold the key to deciphering the underlying patterns and relationships that govern the data. The process of clustering, a fundamental task in unsupervised machine learning, seeks to group data points based on similarity measures, thereby unveiling these concealed structures. However, traditional clustering methods sometimes fall short when faced with the intricacies of real-world data, characterized by its high-dimensionality and the subtle, latent relationships embedded within. This is where latent models come into play, offering a more nuanced approach to clustering.

Latent models, by design, incorporate latent variables that represent hidden factors influencing the observed data. These models provide a powerful framework for clustering, enabling the discovery of complex patterns that are not readily apparent. Among the most prominent latent models in clustering are Gaussian Mixture Models (GMMs) and Latent Dirichlet Allocation (LDA). GMMs approach clustering through a probabilistic lens, assuming that the data is generated from a mixture of several Gaussian…