What are some best practices and tips for cluster analysis for mixed data types?

1 Choose a suitable distance measure

One of the key steps in cluster analysis is to measure the similarity or dissimilarity between data points. For numerical variables, the Euclidean distance is a common choice. However, for categorical or ordinal variables, the Euclidean distance may not be appropriate or meaningful. Instead, you can use other distance measures, such as the Gower distance, the Manhattan distance or the Hamming distance. These measures can handle different types of variables and assign weights to them based on their importance.

3 Select a clustering algorithm

There are many clustering algorithms available, such as k-means, hierarchical or density-based methods. However, not all of them can handle mixed data types well. Some of them require numerical variables only, while others can accommodate categorical or ordinal variables. You need to choose a clustering algorithm that matches your data type and your research question. For example, if you want to find clusters based on both numerical and categorical variables, you can use k-prototypes, which is an extension of k-means. If you want to find clusters based on hierarchical relationships, you can use agglomerative clustering with a suitable linkage method.

4 Evaluate and interpret your clusters

The final step in cluster analysis is to evaluate and interpret your clusters. You need to assess how well your clusters fit your data and your research objectives. You can use various criteria, such as the silhouette coefficient, the Calinski-Harabasz index or the Dunn index, to measure the quality of your clusters. You can also use visualization techniques, such as scatter plots, dendrograms or heat maps, to explore the structure and characteristics of your clusters. You should also label your clusters based on their dominant features and explain their meaning and implications.