Attribute reduction method for information system based on MPI parallel solving

An information system and attribute reduction technology, applied in the direction of resource allocation, program control design, instruments, etc., can solve the problems of redundancy and large amount of data, and achieve the effect of solving the problem of long time, improving time and improving computing performance.
CN106598743AActive Publication Date: 2017-04-26CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING UNIV OF POSTS & TELECOMM
Publication Date
2017-04-26

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Abstract

The invention discloses an attribute reduction method for an information system based on MPI parallel solving. The attribute reduction method comprises the following steps: firstly, reading the data of the information system, pretreating numerical values, and carrying out data discretization; then, horizontally dividing the information system into p sample data subsets, assigning to n nodes through communication, calculating the equivalence class of the data subsets in parallel, and integrating the result of each node to obtain m equivalence class division information subsystems of the whole information system; then, assigning the m information subsystems to the n nodes, carrying out parallel attribute core computing until all the information subsystems are processed, then merging the results of the nodes to obtain the attribute core of the whole information system; and finally, carrying out parallel solving attribute reduction, and merging the attribute reduction results of the nodes to obtain the attribute reduction of the whole information system. A rough set attribute reduction method is combined with MPI parallel computing, so that parallel solving can be carried out by using differentiation matrix solving attribute reduction calculation, and the algorithmic efficiency is improved.
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Description

technical field

[0001] The invention belongs to the fields of data mining, rough sets, and parallel computing, and specifically relates to a method for obtaining attribute reduction in parallel based on MPI by using a resolution matrix. Background technique

[0002] With the explosive growth of data in recent years, parallel technology is becoming more and more important. The main purpose of parallel computing is to save processing time for large-scale complex problems or massive data, and to integrate "cheap" computer resources to build a parallel computing platform to overcome the bottleneck of single-computer computing performance. And stand-alone storage space limitations.

[0003] Parallel computing refers to splitting a large-scale computing task into multiple sub-tasks on a parallel computer or parallel computing platform, and assigning them to each processor. purpose of the task. The parallelism of the problem to be processed is the key to optimize the solution usi...

Claims

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