The chapter provides a comprehensive overview of clustering techniques in data mining, emphasizing their role in simplifying large datasets with many attributes. Clustering is described as a form of unsupervised learning that groups similar objects into clusters, offering concise summaries of data. The survey focuses on the challenges and advancements in clustering algorithms, particularly those designed for large databases and high-dimensional data. It highlights the historical and mathematical roots of clustering, its applications in various fields such as statistics, science, pattern recognition, and machine learning, and its importance in data mining. The chapter also introduces notations and concepts related to clustering, including the representation of data points, segments, units, and histograms, and outlines the goal of assigning data points to a finite number of clusters. Finally, it provides a bibliography of general references and specific sources for further reading on clustering techniques.The chapter provides a comprehensive overview of clustering techniques in data mining, emphasizing their role in simplifying large datasets with many attributes. Clustering is described as a form of unsupervised learning that groups similar objects into clusters, offering concise summaries of data. The survey focuses on the challenges and advancements in clustering algorithms, particularly those designed for large databases and high-dimensional data. It highlights the historical and mathematical roots of clustering, its applications in various fields such as statistics, science, pattern recognition, and machine learning, and its importance in data mining. The chapter also introduces notations and concepts related to clustering, including the representation of data points, segments, units, and histograms, and outlines the goal of assigning data points to a finite number of clusters. Finally, it provides a bibliography of general references and specific sources for further reading on clustering techniques.