Non-redundant Multilayer Mining Algorithm for Multidimensional Multilayer Data
摘要
针对传统的挖掘方法一直存在实现过程复杂的问题
提出基于免疫遗传的多维多层数据无冗余跨层挖掘算法
首先通过罗杰斯特回归函数对多维多层数据进行采集
并通过多维多层数据目标域和源域的一致性对多维多层数据进行分类
在此基础上
通过对免疫遗传算法实现多维多层数据无冗余跨层挖掘的基本思想进行分析
确定其编码方案和适应度函数
并通过遗传操作和群体更新
完成基于免疫遗传的多维多层数据无冗余跨层挖掘算法.实验结果表明
使用此算法进行多维多层数据无冗余跨层挖掘
实现过程较为简单
对计算机网络运行速度影响较小
使用范围较广
为该领域的研究发展提供新的思路.
Abstract
According to the traditional mining method has been the realization process of complex problems
proposed multi-dimensional data based on immune genetic algorithm for mining non redundant cross layer
first through the Rodgers regression function to collect the multi-dimensional data
and through the consistency of the multi-dimensional data source and target domain to classify the multi-dimensional data
on the basis of through the analysis
the basic idea of immune genetic algorithm to realize the multi-dimensional data mining non redundant cross layer
determine the encoding scheme and the fitness function
and through genetic manipulation and group updates
complete multi-dimensional data based on immune genetic algorithm for mining non redundant cross layer. The experimental results show that the proposed algorithm for multi-dimensional data mining non redundant cross layer
the implementation process is relatively simple
less impact on the computer network running speed
wide application
to provide new ideas for the development of this field.