Unsupervised learning has emerged as a powerful tool for uncovering hidden patterns and structures within complex datasets, making it indispensable across various domains. This book chapter provides an in-depth exploration of unsupervised learning algorithms, with a particular focus on advanced clustering techniques, including spectral clustering, mean-shift clustering, agglomerative hierarchical clustering, density-based clustering, and fuzzy C-means clustering. The chapter delves into the challenges and limitations of each algorithm, offering insights into their applicability and performance across diverse datasets. Moreover, hybrid approaches combining fuzzy C-means with optimization and ensemble techniques are thoroughly examined, highlighting their potential to enhance clustering accuracy and robustness. The comparative analysis presented in this chapter not only emphasizes the strengths and weaknesses of density-based clustering but also elucidates the strategic application of these algorithms in real-world scenarios. The chapter concludes by addressing research gaps and proposing future directions to advance the field of unsupervised learning.
Unsupervised learning stands out as a critical method in data science and machine learning because it was essential to gleaning valuable insights from large, unlabeled datasets [1]. Unsupervised learning finds patterns and structures in data without the need for prior knowledge, in contrast to supervised learning, which trains models using predetermined labels [2]. This capacity was crucial for identifying hidden connections, breaking down data into insightful categories, and coming up with fresh theories on the distribution of the data at large [3]. Unsupervised learning algorithms are highly versatile and utilized in several fields such as bioinformatics, image processing, and market analysis. As such, valuable resources for both scholars and practitioners [4,5].
Among the various unsupervised learning techniques, clustering algorithms stand out as a primary method for grouping data based on similarity [6]. This chapter focuses on several advanced clustering techniques, each with unique strengths and limitations [7]. Spectral clustering, for instance, leverages graph theory to partition data into clusters based on eigenvalues of similarity matrices, making it effective for identifying clusters with complex shapes [8]. Mean-shift clustering, another powerful technique, employs iterative shifting to locate modes in the data distribution, offering advantages in situations where cluster shapes are unknown or data was noisy [9-11]. These techniques provide a foundation for understanding how clustering can be adapted to different data characteristics and requirements [12].
