I wanted to state the advantages, disadvantages, and limitations of using the Hierarchical clustering algorithm for Project 2.
It is a powerful method for uncovering complex relationships within a dataset, and it has several advantages over other clustering methods. One of its strengths is its ability to handle mixed data types, which is especially useful when analyzing datasets that contain both numerical and categorical variables.
However, as datasets get larger, the computational complexity of Hierarchical Clustering increases, and efficient algorithms become necessary to handle the analysis. It’s also important to carefully choose linkage methods and distance metrics to ensure the dendrogram’s structure is accurate. Linkage methods determine how clusters are merged together, while distance metrics measure the similarity or dissimilarity between data points. Choosing the right combination of linkage method and distance metric is crucial to obtaining meaningful results.
Another consideration when using Hierarchical Clustering is missing data. While the method can handle missing data well, it may not be the best choice for datasets with many missing values. In such cases, imputation techniques may be necessary to fill in the missing values before clustering.
Despite these considerations, the method’s ability to reveal hierarchical relationships makes it an essential algorithm for thorough analysis. Hierarchical Clustering can help identify patterns and relationships within a dataset that may not be immediately apparent. With careful consideration of the factors mentioned above, Hierarchical Clustering can be a powerful algorithm for uncovering complex relationships within a dataset.