In this chapter we discuss how fuzzy logic extends the envelop of the main data mining tasks: clustering, classification, regression and association rules. We begin by presenting a formulation of the data mining using fuzzy logic attributes. Then, for each task, we provide a survey of the main algorithms and a detailed description (i.e. pseudo-code) of the most popular algorithms. However this chapter will not profoundly discuss neuro-fuzzy techniques, assuming that there will be a dedicated chapter for this issue.
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Rokach, L. (2008). The Role of Fuzzy Sets in Data Mining. In: Maimon, O., Rokach, L. (eds) Soft Computing for Knowledge Discovery and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-69935-6_8
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DOI: https://doi.org/10.1007/978-0-387-69935-6_8
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