This paper provides an overview of pattern clustering methods from a statistical pattern recognition perspective, aiming to offer useful advice and references to fundamental concepts for clustering practitioners. It presents a taxonomy of clustering techniques, identifies cross-cutting themes and recent advances, and describes important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval. The paper discusses the components of a clustering task, including pattern representation, feature selection and extraction, similarity measures, and clustering techniques. It covers hierarchical clustering algorithms, partitional algorithms, mixture-resolving and mode-seeking algorithms, nearest neighbor clustering, fuzzy clustering, and other clustering methods. The paper also addresses the evaluation of clustering results, the role of domain expertise, and the challenges of clustering large data sets. It highlights the importance of similarity measures, the differences between clustering and discriminant analysis, and the various applications of clustering in fields such as image analysis, data mining, and information retrieval. The paper concludes with a discussion of the challenges and future directions in clustering research.This paper provides an overview of pattern clustering methods from a statistical pattern recognition perspective, aiming to offer useful advice and references to fundamental concepts for clustering practitioners. It presents a taxonomy of clustering techniques, identifies cross-cutting themes and recent advances, and describes important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval. The paper discusses the components of a clustering task, including pattern representation, feature selection and extraction, similarity measures, and clustering techniques. It covers hierarchical clustering algorithms, partitional algorithms, mixture-resolving and mode-seeking algorithms, nearest neighbor clustering, fuzzy clustering, and other clustering methods. The paper also addresses the evaluation of clustering results, the role of domain expertise, and the challenges of clustering large data sets. It highlights the importance of similarity measures, the differences between clustering and discriminant analysis, and the various applications of clustering in fields such as image analysis, data mining, and information retrieval. The paper concludes with a discussion of the challenges and future directions in clustering research.