How can you perform cluster analysis for a dataset with unknown groups?

I found that visualizing your data before choosing a clustering algorithm offers a preliminary snapshot of the inherent data patterns, guiding your selection process. This initial graphical representation can hint at cluster densities, shapes, and potential outliers, enabling a more informed decision about which algorithm might be best suited for the dataset in question.

Visualize the given attributes using density plots. Hints of clusters manifest as mutiple peaks. Multiple peaks across multiple variables calls for a bivariate scatter plot analysis which may reveal the clusters more strongly if they are truly present. I start exploring these clusters using simple KMeans. However, in real datasets clusters can have wierd shapes and Kmeans may not be optimal. Hence I explore all the clustering strategies I am aware of. What finally decides whether there are natural clusters or not is when we try to understand the clusters and assign a logical label. If I am unable to lable them i.e. undertand what the clusters represent, then the whold exercise is wasted. Caution:Multi-peaks maybe due to missing values.

Preprocessing is critical. One important step is standardizing or normalizing the variables to ensure all variables get equal representation in the definition of clusters. This step may sometimes impact clustering negatively (Refer to Tibshirani on clustering). If using Hierarchical clustering, the distance calculation method impacts the cluster definitions. Dimentionality reduction is dropping attributes that do not seem to indicate any clusters (multiple peaks in density distribution). This step may make the clusters definitions on remaining dimensions sharp. Use PCA if you can make sense of the resulting clusters.

By selecting an appropriate algorithm, such as K-Means or Hierarchical clustering, and set the number of clusters as a starting point. Utilize dimensionality reduction techniques like PCA to enhance feature selection. Iteratively refine cluster numbers using validation metrics like the silhouette score or elbow method. Experiment with various distance measures to assess similarity. For robust results, employ ensemble clustering or DBSCAN for irregularly shaped clusters. Regularly visualize your clusters for insights. Lastly, remember that cluster analysis is iterative; adjust parameters, validate, and refine until meaningful groupings emerge, revealing hidden patterns in your data.