Table 5. Clustering solutions for mixed-type data with single and mixed distance metrics and 3 clustering algorithms.

Mixed distance metrics handle data mixtures by implementing multiple distance metrics targeted towards specific data types.

Dissimilarity Method	Clustering Algorithm	Distance Metric	Data Type Target
Manhattan distance	Hierarchical clustering PAM SOM	Manhattan distance (single)	Binary, categorical, continuous
Euclidean distance	Hierarchical clustering PAM SOM	Euclidean distance (single)	Binary, categorical, continuous
DAISY	Hierarchical clustering PAM	Gower coefficient Euclidean	Categorical, binary Continuous
Mercator	Hierarchical clustering PAM	Jaccard Sokal-Michener Gower coefficient Manhattan Euclidean	Binary Binary Nominal Ordinal Continuous
Supersom	SOM	Manhattan Euclidean	Categorical, binary Continuous