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Matrix dynamic attribute reduction method with simultaneous addition of object and attribute

A technology of attribute reduction and incremental matrix, applied in the direction of complex mathematical operations, etc., can solve problems such as abstraction and complexity of calculation and representation

Pending Publication Date: 2018-12-21
YUNCHENG UNIVERISTY
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Problems solved by technology

[0003] Since the traditional attribute reduction algorithm is mainly implemented by using the intersection and union operations of sets in mathematics, the calculation and representation forms are relatively abstract and complex

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  • Matrix dynamic attribute reduction method with simultaneous addition of object and attribute
  • Matrix dynamic attribute reduction method with simultaneous addition of object and attribute
  • Matrix dynamic attribute reduction method with simultaneous addition of object and attribute

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Embodiment Construction

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0026] refer to figure 1 , the embodiment of the present invention provides a matrix dynamic attribute reduction method in which objects and attributes are simultaneously increased, and the method includes the following steps:

[0027] Step 1, input the decision table before the change, the incremental object set U X , the incremental attribute set P, the relative knowledge granularity GP of the decision table before the change U (D|C), condition attribute e...

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Abstract

The invention discloses a matrix dynamic attribute reduction method in which objects and attributes are simultaneously added, which relates to the technical field of rough set and granular computing theory in data mining. Firstly, the minimum attribute reduction REDU of a decision table before change is calculated. When objects and attributes are added to the decision table at the same time, whether the relative knowledge granularity of the minimal attribute reduction of the decision table is equal to the relative knowledge granularity of the changed decision table is calculated, If so, REDU is the minimum attribute reduction after the change, otherwise, the matrix method and incremental mechanism are used to calculate the external importance of all attributes of the changed decision tableexcept REDU, Select the largest attribute to add to the minimum attribute reduction, calculate the relative knowledge granularity until it is equal to the relative knowledge granularity of the changed decision table, delete the redundant attributes, and obtain the minimum attribute reduction of the changed decision table. The invention effectively solves the problem that the minimum attribute reduction can be quickly calculated when the objects and the attributes in the decision table are simultaneously increased, and contributes to improving the efficiency of the dynamic data knowledge mining.

Description

technical field [0001] The invention relates to the technical field of rough set and granular computing theory in data mining, in particular to a matrix dynamic attribute reduction method for simultaneously increasing objects and attributes. Background technique [0002] At present, with the rapid development of computer network, storage and communication technology, all walks of life have accumulated a variety of massive application data, and these application data are constantly changing dynamically in reality. Traditional attribute reduction algorithms are used to calculate these dynamic data. Repeated calculations are required, resulting in huge consumption of running time, which makes traditional data mining methods unable to solve the problem of knowledge discovery in dynamic data in a timely and effective manner. Incremental learning technology can simulate the cognitive mechanism of human beings, and can make full use of the original operation results to dynamically ...

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Application Information

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IPC IPC(8): G06F17/16
CPCG06F17/16
Inventor 景运革王春红王宝丽
Owner YUNCHENG UNIVERISTY
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