Abstract
Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Many types of knowledge and technology have been proposed for data mining. Among them, finding association rules from transaction data is most commonly seen. Most studies have shown how binary valued transaction data may be handled. Transaction data in real-world applications, however, usually consist of fuzzy and quantitative values, so designing sophisticated data-mining algorithms able to deal with various types of data presents a challenge to workers in this research field. This chapter thus surveys some fuzzy mining concepts and techniques related to association-rule discovery. The motivation from crisp mining to fuzzy mining will be first described. Some crisp mining techniques for handling quantitative data will then be briefly reviewed. Several fuzzy mining techniques, including mining fuzzy association rules, mining fuzzy generalized association rules, mining both membership functions and fuzzy association rules, will then be described. The advantages and the limitations of fuzzy mining will also be discussed.
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Hong, TP., Lee, YC. (2008). An Overview of Mining Fuzzy Association Rules. In: Bustince, H., Herrera, F., Montero, J. (eds) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73723-0_20
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DOI: https://doi.org/10.1007/978-3-540-73723-0_20
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