Cluster analysis is a multivariate statistical technique used to classify a set of objects, individuals, observations, or variables into relatively homogeneous groups called clusters. The main objective is to ensure that objects within the same cluster are more similar to each other than to objects belonging to different clusters. In Research Methodology, cluster analysis is useful when the researcher wants to discover natural groupings or patterns in a large dataset without having predetermined groups. Cluster analysis is an exploratory technique because the groups are not usually known in advance. It is widely used in social sciences, marketing research, psychology, education, business research, and other fields. For example, a researcher studying consumers may use cluster analysis to divide them into groups based on age, income, purchasing behaviour, preferences, and attitudes. Similarly, students can be grouped according to academic performance, learning behaviour, or demographic characteristics. There are several approaches to cluster analysis. Hierarchical clustering creates a tree-like structure of clusters and can be either agglomerative or divisive. In agglomerative clustering, each observation initially forms a separate cluster, and similar clusters are gradually combined. In divisive clustering, all observations begin in one cluster and are progressively divided. Non-hierarchical methods, such as K-means clustering, directly divide observations into a specified number of clusters. The first step in conducting cluster analysis is to clearly define the research problem and objectives. The researcher must determine what objects or respondents are to be grouped and why clustering is