This dissertation provides an introduction about self - organizing data mining technology for the investor in the stock market , and we hope our research can help them to improve the decision making based on analyzing data 不论是参数型还是非参数型的自组织数据挖掘,本文的实证研究为其在股票市场中的应用提供了切实可行的经验。
Use data source view designer in business intelligence development studio to edit various properties of a data source view and to organize the objects defined in a data source view in multiple diagrams 使用business intelligence development studio中的数据源视图设计器，可以编辑数据源视图的各种属性，并在多个关系图中组织数据源视图中所定义的对象。
The result unveils the difference and relationship between the self - organizing data mining and regression analysis , it also proves that self - organizing data mining is an efficient approach to the research on approximating and forecasting of complex systems 结果不仅揭示了二者的区别和联系，而且表明，自组织数据挖掘方法是复杂系统模拟预测的有效工具。
But to the parallel calculation and distributed storage themselves , how to organize the distributed storage of data in reason and how to perform the parallel processing effectively are a task to be solved urgently at present 但就数据分布式存储与并行处理本身来说，如何合理有效地组织数据的分布式存储与并行处理无论在理论上还是在技术上都有许多问题需要研究。
To prepare data for a later operation . editing may include the rearrangement or the addition of data , the deletion of unwanted data , format control , code conversion , and the application of standard processes such as zero suppression 为后继操作准备数据，编辑可以包括重新组织数据或增加数据、删除不需要的数据、格式控制、代码转换以及使用诸如消零那样的规格化处理。
The major achievements in this paper are as follows : ( 1 ) a new viewpoint is presented : the method of self - organizing data mining is a valid technique for realizing the method of the qualitative to quantitative meta - synthesis using to complex system 其主要工作如下： ( 1 )提出了一个新观点：自组织数据挖掘方法是研究复杂系统的从定性到定量综合集成方法的有效实现技术。
Conception hierarchy tree classifiers which is a statistical approach have played an important role in attribute - oriented induction . it can help us discover the characteristics of data , make them more understandable and organized in concept - oriented structure 通过它对数据库中的数据进行分类可以帮助我们发现数据的特征，以更加容易理解的方式总结数据，并且依据面向概念的结构来组织数据。
By adding data samples of same type , we have solved one contradiction in application of self - organizing data mining algorithm : its analog complexing algorithm needs large data samples while there are only narrow data samples in complex economy system 使用增加同类经济对象数据样本的方法，解决了自组织数据挖掘算法实际应用的一个矛盾：其相似体合成算法需要大数据样本但复杂经济系统中有效数据样本不足。
The first algorithm uses an iterative self - organizing data analysis technique and fuzzy clustering analysis theory . it is fast , simple and easy for programming , but more suitable for small system . the second one is a recursive algorithm 首先采用模糊聚类分析中的迭代自组织数据分析技术（ iterativeself - organizingdataanalysistechniquea ） ，提出了改进isodata不良数据辨识法；其次，提出了递推不良数据辨识法。
The qualitative analysis , theoretic analysis and experimental research are exerted to do the comparison between the above two methods from three aspects , including the algorithm process , the approximating and forecasting effect in different objects , and the relationship 摘要采取定性分析、理论分析和实验研究相结合的方式，从三方面对自组织数据挖掘方法与回归分析方法进行比较：包括二者的算法过程，对不同对象的拟合和预测效果以及二者的联系。
