Clustering is an important data mining technique used to group similar data objects into meaningful clusters without prior knowledge of class labels. Different clustering techniques are applied depending on the structure of data, size of datasets, and the presence of noise. Partitioning, hierarchical, and density-based clustering are three major categories of clustering methods used in data warehousing and data mining. Partitioning clustering techniques divide a dataset into a predefined number of non-overlapping clusters. Each data object belongs to exactly one cluster, and the objective is to maximize similarity among objects within the same cluster while minimizing similarity between different clusters. These techniques require the number of clusters to be specified in advance. They are efficient for large datasets but are sensitive to initial conditions and outliers. K-means is the most widely used partitioning clustering algorithm. Hierarchical clustering creates a tree-like structure called a dendrogram that represents relationships among data objects. It can follow two approaches: agglomerative and divisive. Agglomerative clustering begins with each object as an individual cluster and gradually merges similar clusters until a single cluster is formed. Divisive clustering starts with one large cluster and repeatedly divides it into smaller clusters. Hierarchical methods do not always require the number of clusters to be predefined and provide a detailed view of data relationships. However, they are computationally expensive for large datasets and are sensitive to noise and outliers. Density-based clustering techniques form clusters based on the density of