Mining method for fuzzy rough monotonic data based on inclusion degree

A data mining, fuzzy and rough technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems that the importance of attributes is not easy to observe, the nuclear attributes are not easy to find, and the decision table is inconsistent.

Inactive Publication Date: 2012-07-25
广州锦灵信息科技有限公司
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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

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  • Mining method for fuzzy rough monotonic data based on inclusion degree
  • Mining method for fuzzy rough monotonic data based on inclusion degree
  • Mining method for fuzzy rough monotonic data based on inclusion degree

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Embodiment Construction

[0060] The present invention will be further described below in conjunction with the accompanying drawings.

[0061] The implementation process of the present invention is as Figure 1-2 As shown, the specific steps include:

[0062] A fuzzy rough monotone data mining method based on inclusion, including:

[0063] (1) The decision attribute D is reordered to form an ordered set D'; the condition attribute C is reordered to form an ordered set C';

[0064] (2) The object set U obtains the ordered set U of object rearrangement according to D′ D , the object set U according to C′, get the ordered set U of object rearrangement i ;

[0065] (3) According to U D and U i Determine the relationship between the decision attribute and condition attribute value of the object in the object, set the judgment rule, and judge the relationship between the decision attribute and the condition attribute, so as to establish a fuzzy inclusion monotone dependency model;

[0066] The fuzzy c...

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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 ...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 梁瑾
Owner 广州锦灵信息科技有限公司
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