Abstract
To overcome the disadvantage of determining artificially the class number, fuzzy C means clustering is introduced to fuzzify the continual attribute, and the best minute class number is obtained by cluster validity analysis. The relationship of glass composition and its application is excavated using data mining method in this paper.
Highlights
Data Mining is defined as a large number of incomplete, noisy, fuzzy and random data extracted implicit in which people do not know in advance, but potentially useful information and knowledge, such as concepts, knowledge rules, restrictions, laws and so on
Rough set theory has been successfully applied to relational database data mining, such as A Fuzzy Search Method for Rough Sets in Data Mining, Mining Stock Price using Fuzzy Rough Set System and so on (Osei Adjei and LiChen, 2001, 980)
The continuous attributes must be discrete before extracting the rules using rough set theory
Summary
Data Mining is defined as a large number of incomplete, noisy, fuzzy and random data extracted implicit in which people do not know in advance, but potentially useful information and knowledge, such as concepts, knowledge rules, restrictions, laws and so on. Rough set theory has been successfully applied to relational database data mining, such as A Fuzzy Search Method for Rough Sets in Data Mining, Mining Stock Price using Fuzzy Rough Set System and so on (Osei Adjei and LiChen , 2001, 980). The continuous attributes must be discrete before extracting the rules using rough set theory. This process will result in some degree of information loss because discrete attribute values will not be retained in property values in the actual existence of numerical differences. French scholar D.Dubios and H.Prad presented the definition of fuzzy rough set combined rough set and fuzzy set to solve the problem of information loss in the course of attribute discrete based on rough set.
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