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A. A. Freitas, “A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery,” Advances in Evolutionary Computing—Natural Computing Series, Part II, Springer-Verlag, New York, 2003, pp. 819-845.

has been cited by the following article:

• TITLE: A Study on Associated Rules and Fuzzy Partitions for Classification

  AUTHORS: Yeu-Shiang Huang, Jyi-Feng Yao

  KEYWORDS: Data Mining; Fuzzy Partition; Partial Classification; Association Rule; Knowledge Discovery.

  JOURNAL NAME: Intelligent Information Management, Vol.4 No.5, September 27, 2012

  ABSTRACT: The amount of data for decision making has increased tremendously in the age of the digital economy. Decision makers who fail to proficiently manipulate the data produced may make incorrect decisions and therefore harm their business. Thus, the task of extracting and classifying the useful information efficiently and effectively from huge amounts of computational data is of special importance. In this paper, we consider that the attributes of data could be both crisp and fuzzy. By examining the suitable partial data, segments with different classes are formed, then a multithreaded computation is performed to generate crisp rules (if possible), and finally, the fuzzy partition technique is employed to deal with the fuzzy attributes for classification. The rules generated in classifying the overall data can be used to gain more knowledge from the data collected.