Cluster analysis involves several steps, starting with data collection and preprocessing. Once the data is ready, different clustering algorithms, such as K-means, hierarchical clustering, or DBSCAN, can be applied. These algorithms attempt to partition the data into clusters based on similarity metrics, which can include distance measures like Euclidean or Manhattan distance. The choice of algorithm and metrics often depends on the specific characteristics of the dataset and the research question being addressed.
