A Study on Associated Rules and Fuzzy Partitions for Classification

Yeu-Shiang Huang, Jyi-Feng Yao

Department of Industrial and Information Management, National Cheng Kung University, Chinese Taiwan.

DOI: 10.4236/iim.2012.45032                

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.

Keywords

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

Conflicts of Interest

The authors declare no conflicts of interest.

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