A data fusion method for reducing redundancy features of discrete data sets
A discrete data and data fusion technology, applied in the direction of instruments, computing, character and pattern recognition, etc., can solve the problems of insufficient computing power and long computing time, and achieve the goal of shortening computing time, reducing time overhead, and efficient division of labor and communication mechanism Effect
Inactive Publication Date: 2019-03-08
STATE GRID CORP OF CHINA +3
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[0004] In view of the above defects or improvement needs of the prior art, the present invention provides a data fusion method for reducing redundant features of discrete data sets, thereby solving the technical problems of insufficient computing power and excessively long computing time in the prior art
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[0035] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.
[0036] First define a binary relation, for U×U{1 , x 2 >|x 1 , x 2 ∈}U, if R is called a binary relation on U, R has the following properties, if R is reflexive on U Then there is xRx; if R is symmetric on U If xRy, then yRx; if R is transitive on U If xRy and yRz, then there is xRz, and R is an equivalence relation on U if and only if R satisfies reciprocity, symmetry and transitivity...
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The invention discloses a data fusion method for reducing redundancy features of a discrete data set, belonging to the field of data fusion analysis. Firstly, the invention calculates and analyzes theclassification results of the data set under each individual attribute and the decision attribute and the condition attribute, and judges whether the discrete data set has the potential of reductionand fusion. Then the kernel attribute set of the discrete data set and the classification results based on the kernel attribute are calculated and the bidirectional reduction process is carried out, and the minimum attribute set of the discrete data set is obtained, which realizes the feature reduction and fusion of the discrete data set. Compared with the classical rough set theory, the feature reduction fusion method embodies the efficient division of labor and communication mechanism, and shortens the computational time of the minimal reduction process of the data set. The data set fusion method provided by the invention can be applied to a plurality of types of discrete data set attribute reduction processes, and can effectively reduce the time cost of data set attribute reduction.
Description
technical field [0001] The invention belongs to the field of data fusion analysis, and more specifically relates to a data fusion method for reducing redundant features of discrete data sets. Background technique [0002] With the rapid development of the economy and society, all walks of life have accumulated considerable industry data after a certain period of time, and big data analysis has become an important means of progress. However, in the face of such a huge amount of data, it is very difficult to find useful information from it. It is necessary to use relevant data analysis methods to reduce and fuse the original data set to a certain extent to improve the information of the original data set. Density provides guidance for actual production and life. [0003] Rough set theory was first proposed by Polish scholar Pawlak in 1982. It can examine knowledge from a new perspective without any prior knowledge, obtain decision-making or classification rules of problems t...
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IPC IPC(8): G06K9/62
CPCG06F18/2115G06F18/251G06F18/24
Inventor 周建中刘英赵宇杰杜义刘涵李玲姜伟单亚辉李超顺田弟巍王齐飞
Owner STATE GRID CORP OF CHINA