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A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis

In: Data Mining and Knowledge Discovery via Logic-Based Methods

Author

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  • Evangelos Triantaphyllou

    (Louisiana State University)

Abstract

In many data mining and sub data mining sub knowledge discovery, see data mining knowledge discovery applications a critical task is how to define the values of the various attributes that the analyst believes may be of significance. For easily quantifiable attributes (such as, age, weight, cost, etc.) this task is a rather straightforward one as it involves simple measurements and expressing the results in terms of some units. For other attributes, however, this task may not be a simple one. This is the case when some of the data are fuzzy. For instance, although in common language one often uses terms such as “small,” “large,” “round,” “tall,” and so on, these terms may mean different concepts to different people or to the same person at different times.

Suggested Citation

  • Evangelos Triantaphyllou, 2010. "A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis," Springer Optimization and Its Applications, in: Data Mining and Knowledge Discovery via Logic-Based Methods, chapter 0, pages 297-308, Springer.
    • Handle: RePEc:spr:spochp:978-1-4419-1630-3_16
    DOI: 10.1007/978-1-4419-1630-3_16
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