| 1. | Study and application of olam in dss
中的研究与应用 |
| 2. | Third , the paper discusses the theory of olam and some arithmetic of clustering and regression
在此基础上，从以下几个方面论述了解决方案。 |
| 3. | ( 5 ) the framework of olam is proposed , which can improve atmosphere environment forecast system
（ 5 ）提出一个olam的框架，为系统的进一步完善奠定理论基础。 |
| 4. | Olam ( on - line analytical mining ) integrates olap with data minig , which mines knowledge in multi - dimensional databases or data cube
Olam是数据挖掘( dm )与olap技术的有机结合，充分利用了二者的优势，克服二者的不足。 |
| 5. | This paper is a study for olam and the software system can develop the see - sight of user and efficiency guide the aluminum production
结果表明，所研究、开发的铝电解槽阴、阳极电流分布系统对电解槽的稳定生产起到了重要的指导作用。 |
| 6. | During this research work , we have respectively studied the olam network and the bp network on their basic theories , arithmetic , learning process , learning samples and outcome of spectrum analysis
在本论文工作中，分别研讨了olam网络和bp网络的基本原理、算法、学习过程、学习样本的生成和解谱结果的优劣等内容。 |
| 7. | The software system can analysis different clustering and different point in the same clustering . and give the possible relation and change trend . the olap and dm adopt concurrent data model and the same revelation mode
本系统通过将olap与dm结合建立一致的数据模型和统一展示方式，对北方工业大学硕士论文olam进行了理论上和实践上的研究。 |
| 8. | So we bring forward the method of on - line analysis mining ( olam ) based on trst , with which users can take part in the process of the mining and dynamically decide the objects and the task of mining
由此，提出了基于容错粗集理论的联机分析挖掘技术的理论，使得用户在挖掘的过程中，动态地提出挖掘的对象和要求，系统不断地调整挖掘算法，实施再挖掘。 |
| 9. | 3 ) the concept of data warehouse and olap technology are introduced , and the system structure of olam is built on olap and association rules mining algorithms . , and the system is implemented in air quality forecasting system
3 )引入了数据仓库的概念和olap技术，以它们作为基础，结合关联规则挖掘算法，形成了olam的系统结构，并在空气质量预测系统中初步地实现。 |
| 10. | ( 4 ) the object of data mining based on trst is built on the data warehouse , and olap is joined with data mining based on trst , which is feasible because data mining based on trst does not initialize the quantification
（ 4 ）提出了将容错粗集理论的挖掘对象建立在数据仓库上，与olap技术相结合，即olam技术，并证明这是可行的，因为基于容错粗集理论的数据挖掘技术不再进行初始量化。 |
