Progress in clustering algorithms for astronomical spectra over a decade

As large-scale astronomical surveys such as the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) generate increasingly complex datasets, clustering algorithms have become vital for identifying patterns and classifying celestial objects. This paper systematically investigates the application of five main aspects regarding clustering techniques: partition-based, densitybased, model-based, hierarchical, and others across a range of astronomical research over the past decade. The review focuses on six key application areas: stellar classification, galaxy structure analysis, detection of galactic and interstellar features, high-energy astrophysics, exoplanet studies, and anomaly detection. This paper provides in-depth analysis of the performance and results of each method considering their suitability for different data types. Additionally, it presents clustering algorithm selection strategies based on the characteristics of spectral data. The review highlights challenges such as handling large datasets, the need for more efficient computational tools, and the lack of labeled data. It also underscores the potential of unsupervised and semi-supervised clustering approaches to overcome these challenges, offering insight into their practical applications, performance, and results in astronomical research.