Domain-Specific Particularities of Data Mining: Lessons Learned

Numerous data mining methods and tools have been developed and applied during the last two decades. Researchers have usually focused on extracting new knowledge from raw data, using a large number of methods and algorithms. In areas such as medicine, few of these DM systems have been widely accepted and adopted. In contrast, DM has obtained a considerable success in recent genomic research, contributing to the huge tasks of data analysis linked to the human genome project and related research. This paper presents a study of relevant past research in biomedical DM. It is proposed that traditional approaches used in medical DM should apply some of the lessons learned in decades of research in disciplines such as epidemiology and medical statistics. In this context, novel methodologies will be needed for data analysis in the areas related to genomic medicine, where genomic and clinical data will be tightly collected and studied. Some ideas are proposed for new research design, considering those lessons learned during the last decades.