Knowledge discovery in databases ( kdd ) , a new generation of tools and techniques for automatic and intelligent data analysis , is currently an active research area 数据库中的知识发现( kdd )主要是研究如何自动地和智能地从海量的数据中提取出有用的知识和信息，它是目前相当活跃的一个研究领域。
In recent years , know1edge discovery in databases ( kdd ) has become an active research field , which can fifld its relevance to artificia1 inte11igence , database system and other discip1ines 近年来，数据库中的知识发现( knowledgediscoveryindatabases ， kdd )已成为涉及人工智能和数据库等学科的一门非常活跃的研究领域。
Knowledge discovery in databases ( kdd ) is a multidisciplinary field , drawing work from areas including database technology , artificial intelligence , machine learning , statistics , neural networks , and pattern recognition 数据库中的知识发现( knowledgediscoveryindatabases ， kdd )是当前涉及人工智能、数据库等学科的一门非常活跃的研究领域。
Data mining , also named as kdd ( knowledge discovery in database ) , is an advanced process , in which we can pick up many trustful , novel , useful and readable patterns from very large amounts of data 数据挖掘，也可以称为数据库中的知识发现( knowledgediscoveryindatabase ， kdd ) ，是从大量数据中提取出可信、新颖、有效并能被人理解的模式的高级处理过程。
So a new research field named knowledge discovery , comes into being . because usually the information containing knowledge is stored in database , knowledge discovery in database ( kdd ) becomes the focus of knowledge discovery 因此，一个新的研究领域? ?知识发现应运而生，由于蕴藏知识的信息大都存储于数据库中，数据库中的知识发现( knowledgediscoveryindatabase ， kdd )已成为当前知识发现的一个热点研究课题。
Combining with the using of freight agent management system , which developed by sunnysoft ( dalian ) co , ltd , this paper studied the process of discovering the hiden and unknown informations by the advanced technology of kdd . this paper contained five parts 本文结合盛鸿软件(大连)有限公司开发的货运代理管理系统在实际应用中的情况，研究了利用先进的数据库中的知识发现技术来挖掘隐藏在数据背后的、不为人们所知的信息的过程。
Data mining ( dm ) , also named as kdd ( knowledge discovery in database ) is a hot topic both in research and development , by which we can pick up many trustful , novel , useful and readable patterns from very large amounts of data 数据挖掘，也称为数据库中的知识发现，是从大量数据中提取出可信、新颖、有效并能被人理解的模式的高级处理过程，是数据库技术、人工智能、机器学习、统计分析、模糊逻辑、模式识别、人工神经网络等多个学科相结合的产物。
Knowledge discovery in databases ( kdd ) is a focus topic in research fields of databases and artificial intelligence , which aims to nontrivial of mining the interesting potentially useful , valid and understandable knowledge from data . the high - efficient approaches are the research focus 数据库中的知识发现( knowledgediscoveryindatabases ，简称kdd )是数据库和人工智能领域研究的热点课题，其目的是在数据库中提取隐含的，先前未知的，潜在有用的知识。
In this paper , a complete analysis and dissertation about kdd is put forward , and detailedly discuss an important technology of data mining - artificial neural network . an improved project is presented about bp algorithm and this project improves the learning speed of the neural network 在本文中，作者对kdd （数据库中的知识发现）进行了全面的分析与论述，并对数据采掘的主要技术之一神经网络的bp算法进行了详细的论述，并针对其不足之处提出了改进方案，最终提高了神经网络的收敛速度。
In the end , the thesis proposed a system model for knowledge discovery in agriculture database based on rough sets theory . since the research of knowledge " discovery in agriculture database is still in the early development , the whole solution proposed in this thesis is excepted to provide certain cognition and sample method for current knowledge discovery in agriculture database research . 数据库中的知识发现研究仍然处于起步阶段，它在农业领域中还有许多问题值得探讨。本文所提出的基于粗糙集理论的农业数据库知识发现方案试图为我国农业数据库的知识发现领域提供一种新的认识和尝试性方法。
