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Data mining method of rough set and optimization neural network

A neural network and data mining technology, applied in the field of data mining using rough sets to optimize neural networks, can solve problems such as low rough set processing efficiency, improve learning efficiency and accuracy, reduce data volume, and improve efficiency.

Inactive Publication Date: 2011-02-02
江苏瑞蚨通软件科技有限公司(中外合资)
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in practical applications, for some large-scale networks, the processing efficiency of rough sets is still relatively low.

Method used

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  • Data mining method of rough set and optimization neural network
  • Data mining method of rough set and optimization neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] Embodiment 1: as figure 1 As shown, a rough set optimization neural network data mining method includes the following steps:

[0023] a. Analyze the sample data and form an initial continuous attribute decision table based on known domain knowledge;

[0024] b. Use the discrete method to discretize the continuous attributes;

[0025] c. Reducing the data;

[0026] d. Use the neural network to train and mine the data.

[0027] Step c is achieved by:

[0028] Use the genetic algorithm-based parallel reduction algorithm to reduce the attributes of the data, that is, horizontal reduction, use the reduced attributes as the input layer neurons, and then perform vertical reduction on the data to eliminate inconsistent objects and redundancy in the data object.

[0029] The attribute reduction adopts the following process:

[0030] Input: condition attribute set C={Y11, Y12, ..., Y53}, decision attribute set D={d};

[0031] Output: an attribute reduction set REDU;

[00...

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Abstract

The invention discloses a data mining (DM) method of a rough set and optimization neural network, which is characterized by comprising the following steps: a. analyzing sample data and forming an initial continuous attribute decision table according to the known domain knowledge; b. dispersing the continuous attribute through a dispersion method to form a dispersion attribute decision table; c. reducing the dispersion attribute decision table; and d. training the data by a neural network. The method of the invention can reduce the data amount required by the network learning, and further enhance the DM efficiency of the large actual data base by the application of the rough set and neural network.

Description

technical field [0001] The invention relates to a data mining method, in particular to a data mining method using rough sets to optimize neural networks. Background technique [0002] With the expansion of the application range of the database, a large amount of data is collected into the database every day, how to provide effective data quickly and accurately has become the primary problem to be solved by the system. [0003] Rough set theory is a mathematical tool for describing incomplete and uncertain information. It can effectively analyze and deal with incomplete information such as inaccuracy, inconsistency, incompleteness, etc., and discover hidden knowledge and reveal potential information. law. Rough set theory is based on the method of classifying and observing the data obtained by observation and measurement. It believes that knowledge is based on the ability to classify objects, and knowledge is directly related to different classification patterns related to t...

Claims

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Application Information

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IPC IPC(8): G06N3/02G06F17/30
Inventor 李星
Owner 江苏瑞蚨通软件科技有限公司(中外合资)
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