Clustering Techniques in Data Mining: A Survey of Methods, Challenges, and Applications

Abstract

Clustering is a crucial technique in both research and practical applications of data mining. It has traditionally functioned as a pivotal analytical technique, facilitating the organization of unlabeled data to extract meaningful insights. The inherent complexity of clustering challenges has led to the development of a variety of clustering algorithms. Each of these algorithms is tailored to address specific data clustering scenarios. In this context, this paper provides a thorough analysis of clustering techniques in data mining, including their challenges and applications in various domains. It also undertakes an extensive exploration of the strengths and limitations characterizing distinct clustering methodologies, encompassing distance-based, hierarchical, grid-based, and density-based algorithms. Additionally, it explains numerous examples of clustering algorithms and their empirical results in various domains, including but not limited to healthcare, image processing, text and document clustering, and the field of big data analytics.