Abstract
Recently, some machine learning methods are applied to find regularities and chunks of knowledge hidden in data. It is proven that combined (hybrid) application of various machine learning algorithms may supply more profound understanding of the investigated processes or phenomena. This is particularly true for various branches of business, like banking and finance, retail and marketing, management and logistics. In this chapter different ways of knowledge discovery (aimed at finding the quasi-optimal learning model) and its basic steps are dealt with. Then, a short background for three fundamental approaches of data mining (classification studies, clustering studies and visualization studies) is given, assigning particular attention to problem of visualization of multidimensional data (further called virtual visualization). In the last part of the text, using a set of business data extracted from a large anonymous database, various machine learning algorithms are used to exemplify hybrid (combined) extraction of useful knowledge.
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Hippe, Z.S. (2002). Hybrid Methodology of Knowledge Discovery for Business Information. In: Abramowicz, W., Zurada, J. (eds) Knowledge Discovery for Business Information Systems. The International Series in Engineering and Computer Science, vol 600. Springer, Boston, MA. https://doi.org/10.1007/0-306-46991-X_5
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DOI: https://doi.org/10.1007/0-306-46991-X_5
Publisher Name: Springer, Boston, MA
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