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Data mining has been a hot topic that attracts both database and machine learning researchers. Due to the constant growth of data volume there is an increasing need to obtain knowledge from these large data sets that are very difficult to handle and process with traditional methods. Software agents can play a significant role performing data mining processes in ways that are more efficient. For instance, they can work to perform selection, extraction, preprocessing and integration of data as well as parallel, distributed, or multisource mining. This paper proposes a framework based on multi-agent systems to apply data mining techniques to health data sets. For a first usage scenario, we use a data set for hypothyroidism and run two mining processes in parallel.

Generating Software Agents for Data Mining: An Example for the Health Data Area