Fuzzy rough monotone dependent data mining method based on decision table

Abstract

The invention provides a fuzzy rough monotone dependent data mining method based on a decision table referencing to a theory of a fuzzy rough set. By interval mapping, a fuzzy rough monotone dependent concept based on the decision table is redefined and is used for mining the fuzzy rough monotone dependent relationship between condition attribute and decision attribute and used for providing a condition input attribute having important influence on the increase or decrease of the decision output, which is mined by using the fuzzy monotone relationship; with the self limitation, the equivalence relationship and other methods are not easy to establish the fuzzy monotone relationship between the input and the output; and the fuzzy monotone relationship is more general in a complex input and output environment as compared with the equivalence relationship and the strict monotone relationship. Therefore, the limitation of the existing methods is improved by the method.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a fuzzy rough monotonous dependent data mining method based on a decision table. Background technique [0002] Rough set theory is a mathematical tool used to deal with uncertain and incomplete data information, and fuzzy set can also describe the uncertainty of information and knowledge. Since the two are highly complementary, they can be combined to analyze information. Deal with uncertainty. In the decision table, rough set mining, the dependency relationship between conditional attributes and decision attributes, reducing attributes, finding out which conditional attributes are more important to decision attributes, the main theoretical basis is the equivalence relationship, due to the limitations of the equivalence relationship Many people proposed different reduction relations, T.Y.Lin et al. proposed domain and compatibility relations, S. Greco et al. proposed dominan...

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Problems solved by technology

[0003] In 1997 and 1998, C.J.Wu and Te-Shun Chou respectively introduced and discussed the fuzzy monotone function and its application in logic control. Some documents discussed the theory related to fuzzy monotone in the Mamdani-Assilians model and T-S inference method. In recent years Many people have discussed many algorithms for decision table attribute reduction, etc. In the decision table, assuming that the increase and decrease of the decision attribute quantity depends on the increase and decrease of certain conditional attribute quantities, then it is necessary to mine the decision attribute quantity. Change the conditional attribute that has an important impact. It is said that there is an important monotonic dependency between such a decision attribute and the conditional attribute, and this monotonic dependency is not necessarily strictly monotonic in the decision table, that is to say, the condition of two adjacent points The monotonicity of the attribute value does not necessarily map to the monotonicity of the corresponding two points of the decision attribute, because there are various interference factors and errors in the actual data, but the existing technology has not been able to effectively mine the influence of the change of the decision attribute. The conditional attributes of important influences, and can affect the decision attribute by controlling these conditional attributes

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