This survey presents a comprehensive review of clustering techniques in data mining. Clustering is the process of dividing data into groups of similar objects. It is a data modeling technique that provides concise summaries of data and is related to many disciplines, playing an important role in various applications. Clustering is particularly useful for large datasets with many attributes, which is a key focus of data mining. The survey focuses on clustering algorithms from a data mining perspective. Clustering is a form of unsupervised learning, where clusters represent hidden patterns in data. It is used to automatically group data points into subsets (clusters) that are similar to each other and different from other subsets. The goal is to assign data points to a finite system of k subsets, with no overlap between subsets and their union covering the entire dataset, except for outliers. Clustering faces challenges in data mining due to large databases, objects with many attributes, and attributes of different types. These challenges require powerful clustering methods that build on classic techniques. The survey discusses these methods and their applications. The survey also introduces notation and concepts used in clustering. Data is represented as a set of points in an attribute space, with each point having multiple attributes. Clustering can be viewed as a density estimation problem and is used in various fields, including statistics, pattern recognition, machine learning, and image processing. The survey highlights the importance of clustering in data mining and its wide range of applications.This survey presents a comprehensive review of clustering techniques in data mining. Clustering is the process of dividing data into groups of similar objects. It is a data modeling technique that provides concise summaries of data and is related to many disciplines, playing an important role in various applications. Clustering is particularly useful for large datasets with many attributes, which is a key focus of data mining. The survey focuses on clustering algorithms from a data mining perspective. Clustering is a form of unsupervised learning, where clusters represent hidden patterns in data. It is used to automatically group data points into subsets (clusters) that are similar to each other and different from other subsets. The goal is to assign data points to a finite system of k subsets, with no overlap between subsets and their union covering the entire dataset, except for outliers. Clustering faces challenges in data mining due to large databases, objects with many attributes, and attributes of different types. These challenges require powerful clustering methods that build on classic techniques. The survey discusses these methods and their applications. The survey also introduces notation and concepts used in clustering. Data is represented as a set of points in an attribute space, with each point having multiple attributes. Clustering can be viewed as a density estimation problem and is used in various fields, including statistics, pattern recognition, machine learning, and image processing. The survey highlights the importance of clustering in data mining and its wide range of applications.