The paper provides an overview of pattern clustering methods from a statistical pattern recognition perspective, aiming to offer useful advice and references to fundamental concepts accessible to practitioners in various fields. It presents a taxonomy of clustering techniques, discusses cross-cutting themes, and highlights recent advances. The authors also describe important applications of clustering algorithms, such as image segmentation, object recognition, and information retrieval. The paper covers various clustering techniques, including hierarchical, partitional, mixture-resolving, and mode-seeking algorithms, as well as nearest neighbor clustering, fuzzy clustering, and representation of clusters. It emphasizes the importance of understanding the differences between clustering and discriminant analysis, and discusses the role of domain knowledge in clustering. The paper concludes with a discussion on the history and future directions of clustering methodology.The paper provides an overview of pattern clustering methods from a statistical pattern recognition perspective, aiming to offer useful advice and references to fundamental concepts accessible to practitioners in various fields. It presents a taxonomy of clustering techniques, discusses cross-cutting themes, and highlights recent advances. The authors also describe important applications of clustering algorithms, such as image segmentation, object recognition, and information retrieval. The paper covers various clustering techniques, including hierarchical, partitional, mixture-resolving, and mode-seeking algorithms, as well as nearest neighbor clustering, fuzzy clustering, and representation of clusters. It emphasizes the importance of understanding the differences between clustering and discriminant analysis, and discusses the role of domain knowledge in clustering. The paper concludes with a discussion on the history and future directions of clustering methodology.