| 一种融合粗集理论和神经网络的分类数据挖掘算法 |
| |
| 引用本文： | 陈秀琼.一种融合粗集理论和神经网络的分类数据挖掘算法[J].三明学院学报,2005,22(2):185-189. |
| |
| 作者姓名： | 陈秀琼 |
| |
| 作者单位： | 三明学院,计算机科学系,福建,三明,350004 |
| |
| 摘 要： | 分类是一种重要的数据分析技术．可以用于提取描述重要数据类的模型和预测未来的数据趋势。本文提出了一种新的粗集理论和神经网络融合的分类方法。不同以往的组合方法，在该方法中粗集理论是挖掘分类知识的主体，最终的分类规则由粗集方法从约简后的决策表中得到，神经网络只是作为一种辅助工具，用来对决策表进行属性约简，并通过删除网络不能分类的数据来对决策表中的噪声进行过滤。与以往方法相比。该方法在保留神经网络高鲁棒性的同时避免了从神经网络中抽取规则的困难。
|
| 关 键 词： | 粗集 神经网络 属性约简 规则抽取 神经网络融合 数据挖掘算法 分类方法 粗集理论 数据分析技术 决策表 |
| 收稿时间： | 2005-05-20 |
| 修稿时间： | 2005-05-20 |
One Sortable Data-Mining Algorithm Integrated with Rough sets Theorem and Neural Network |
| |
| CHEN Xiu-qiong.One Sortable Data-Mining Algorithm Integrated with Rough sets Theorem and Neural Network[J].Journal of Sanming University,2005,22(2):185-189. |
| |
| Authors: | CHEN Xiu-qiong |
| |
| Institution: | Computer Science Department, Sanming College, Sanming 365004, China |
| |
| Abstract: | Sortation, which can be used to extract, describe major data type model and forecast the data tendency, is an important technology for data analysis. The article starts to propose a new sortable data-mining algorithm integrated with a new rough sets theorem and the neural network. Different from the previous grouping method, rough sets theorem is the main part in mining sortation knowledge. The ultimate sotatable principle can be obtained from the decision-making table which is reduced from rough sets. The neural network is only an assistant tool which can be used to reduce attribute of decision-making table. Furthermore, the tool can filter the table noise through deleting the insortable data. Compared to previous skill, this technology can remain the high stability of the neural network. At the same time, it can avoid the difficulty to extract the rule from the neural network. |
| |
| Keywords: | rough sets neural network attribute reduction rule extraction |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
