The new computational algorithms emerging in the data mining literature—in particular, the self-organizing map (SOM) and decision tree analysis (DTA)—offer qualitative researchers a unique set of tools for analyzing health informatics data. The uniqueness of these tools is that although they can be used to find meaningful patterns in large, complex quantitative databases, they are qualitative in orientation. To illustrate the utility of these tools, the authors review the two most popular: the SOM and DTA. They provide a basic definition of health informatics, focusing on how data mining assists this field, and apply the SOM and DTA to a hypothetical example to demonstrate what these tools are and how qualitative researchers can use them.
Castellani, Brian; Castellani, John (2003). Data Mining: Qualitative Analysis with Health Informatics Data. Sage 13(7) 1005-1018. doi: 10.1177/1049732303253523. Retrieved from https://oaks.kent.edu/socpubs/40