Similarities in Fuzzy Data Mining: From a Cognitive View to Real-World Applications

Similarity is a key concept for all attempts to construct humanlike automated systems or assistants to human task solving since they are very natural in the human process of categorization, underlying many natural capabilities such as language understanding, pattern recognition or decision-making. In this paper, we study the use of similarities in data mining, basing our discourse on cognitive approaches of similarity stemming for instance from Tversky's and Rosch's seminal works, among others. We point out a general framework for measures of comparison compatible with these cognitive foundations, and we show that measures of similarity can be involved in all steps of the data mining process. We then focus on fuzzy logic that provides interesting tools for data mining mainly because of its ability to represent imperfect information, which is of crucial importance when databases are complex, large, and contain heterogeneous, imprecise, vague, uncertain or incomplete data. We eventually illustrate our discourse by examples of similarities used in real-world data mining problems.

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