Fast Attribute Reduction Method Based on Rough Classification Knowledge Discovery

Abstract

A fast attribute reduction method based on rough classification knowledge discovery relates to the technical field of data processing and solves the technical problem of simplifying the reduction principle and compressing redundant data the fastest. The specific steps of this method are as follows: 1) Find out the core attributes in the conditional attribute set to form the core attribute set, and the remaining conditional attributes form the candidate attribute set; 2) Determine whether the core attribute set is the reduced solution of the data set, and if so, then Attribute reduction is completed; 3) Use the classification knowledge of the decision attribute set as the standard to evaluate the classification ability of each candidate attribute, and find the candidate with the highest consistency between the classification knowledge combined with the core attribute set and the classification knowledge of the decision attribute set. The selected attributes are moved to the core attribute set; 4) Determine whether the selected attribute set is the reduction solution of the data set, if yes, the attribute reduction is completed, if not, go to step 3. The method provided by the invention is especially suitable for high-dimensional data sets.

Description

technical field [0001] The invention relates to data processing technology, in particular to a technology of fast attribute reduction method based on rough classification knowledge discovery. Background technique [0002] There are mainly two methods for discovering hidden knowledge in large data sets: 1) Data statistics method, which has obvious limitations and defects. 2) Rough theory, which proposes that while keeping the system classification knowledge unchanged, unnecessary attributes and data can be reduced, which improves the data compression processing, but When the number of attributes is large, the theory still has the problem of calculation amount. [0003] In order to overcome the calculation problem caused by the large number of attributes when discovering hidden knowledge in large data sets, a variety of heuristic methods based on rough theory have been developed to select the attributes required by the data set. The most common method is to sort attributes a...

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Problems solved by technology

[0004] Aiming at the above-mentioned defects in the existing technology, the technical problem to be solved by the present invention is to provide a method that can effectively overcome the problem of large amount of calculation, and the principle of reduction is clear and simple, which can make the operation quickly approach the minimum attribute combination, and compress redundant data at the fastest speed. A Fast Attribute Reduction Method Based on Rough Classification Knowledge Discovery for Residual Data

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