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Current research directions are looking at Data Mining (DM) and Knowledge Management (KM) as complementary and interrelated felds, aimed at supporting, with algorithms and tools, the lifecycle of knowledge, including its discovery, formalization, retrieval, reuse, and update. While DM focuses on the extraction of patterns, information, and ultimately knowledge from data (Giudici, 2003; Fayyad et al., 1996; Bellazzi, Zupan, 2008), KM deals with eliciting, representing, and storing explicit knowledge, as well as keeping and externalizing tacit knowledge (Abidi, 2001; Van der Spek, Spijkervet, 1997). Although DM and KM have stemmed from different cultural backgrounds and their methods and tools are different, too, it is now clear that they are dealing with the same fundamental issues, and that they must be combined to effectively support humans in decision making.