Abstract
Data mining and knowledge discovery is a family of computational methods that aim at collecting and analyzing data related to the function of a system of interest for the purpose of gaining a better understanding of it. This system of interest might be artificial or natural. According to the Merriam-Webster online dictionary the term system is derived from the Greek terms syn (plus, with, along with, together, at the same time) and istanai (to cause to stand) and it means a complex entity which is comprised of other more elementary entities which in turn may be comprised of other even more elementary entities and so on. All these entities are somehow interconnected with each other and form a unified whole (the system). Thus, all these entities are related to each other and their collective operation is of interest to the analyst, hence the need to employ data mining and knowledge discovery (DM&KD) methods. Some illustrative examples of various systems are given in the next section.
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Triantaphyllou, E. (2010). Introduction. In: Data Mining and Knowledge Discovery via Logic-Based Methods. Springer Optimization and Its Applications, vol 43. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1630-3_1
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DOI: https://doi.org/10.1007/978-1-4419-1630-3_1
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