Mining method for fuzzy rough monotonic data based on inclusion degree

Abstract

The invention refers to the theory of fuzzy rough set and provides a mining method for fuzzy rough monotonic data based on inclusion degree. The method includes: realigning decision properties and condition attributes according to values; dividing realigned collections into intervals; setting decision rules according to membership function and inclusion degree of each interval; deciding relationships between the decision properties and the condition attributes to build fuzzy included monotonic depending relational models; mining preliminary relationships between the decision properties and the condition attributes via the relational models, setting decision filtering rules, and determining condition attribute reduction data collection and optimal data. Existing attribute reduction algorithm usually aims at limited data collection, the mining method for fuzzy rough monotonic data based on inclusion degree is capable of aiming at massive irregular data, and the larger the data volume is, the more obvious the superiority of the algorithm is.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a fuzzy rough monotone data mining method based on inclusion. Background technique [0002] For the concept and basis of inclusion: [0003] Here C(U) is used to represent the totality of classical sets in U, and F(U) is the totality of fuzzy sets in U. Assume If for any A, B∈F 0 (U) has a number ID (B / A) corresponding, and satisfies: [0004] (1) 0≤ID(B / A)≤1, [0005] (2) ∀ A , B ∈ F 0 ( U ) , A ⊆ B ⇒ ID ( B / A ) = 1 , [0006] (3) For ∀ A , B , H ∈ F 0 ...

AI Technical Summary

Problems solved by technology

[0013] The existing technical models are mainly expanded and changed around the equivalence relationship. Therefore, there are some problems when using these technical models for knowledge reduction and data mining. The details are summarized as follows: (1) In the face of many inputs and Outputting attributes and complex and huge data, how to construct an equivalence relationship between attribute data and some existing extended relationships is a relatively difficult problem; (2) decision tables composed of complex data are generally inconsistent decision tables, and Existing attribute reduction algorithms are generally based on consistent decision tables; (3) Data in complex environments are generally continuous data, and existing attribute reduction algorithms generally have to discretize continuous data , but for irregular complex, variable and large amounts of data, this is a difficult problem; (4) For the existing heuristic knowledge reduction methods, most of the core attributes are used as the starting point, and the relatively important largest attribute is given priority in each step It is required to reduce the result, but because of the problem raised in (1), it is not easy to obtain the core attribute, and it is also difficult to obtain the relative importance in a complex environment, because among the many attributes, the importance of the attribute is not It is easy to observe, and the input and output data are very complex, it is difficult to obtain the relative importance of attributes through artificial statistics or through existing analysis methods; (5) Since the data in complex environments are basically incomplete, This is a difficult problem for the existing attribute reduction methods; (6) The existing attribute reduction algorithms are generally aimed at limited data value sets, and are not suitable for a large number of irregular data value sets. The output data is often a large number of irregular data sets

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