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- Data mining,also known as knowledge discovery in databases. 数据采掘,也称数据库中的知识发现。
- In this paper, knowledge discovery (KD) in relational databases is discussed. 文中讨论了关系数据库中的知识发现。
- They provide effective approximate methods for knowledge discovery in FISs. 进而，给出了这两类积分的主要应用：区间数特征对象的分类与融合。
- This approach enables end-user to self-build knowledge discovery application on SOA4KD. 通过该方法,用户可以自助地在SOA4KD上实现知识发现应用。
- In particular, it plays an increasingly important role in thefield of Data Mining and knowledge Discovery. 近年来它逐步成为数据发掘与知识发现领域中一个不容忽视的方向。
- Outlier detection has always been a hot research field in Knowledge Discovery in Databases (KDD). 孤立点检测一直是知识发现（KDD）中一个活跃的领域，如信用卡欺诈，入侵检测等。
- Association rules is a crucial problem in Knowledge Discovery in Databases（KDD）. 摘要 关联规则是数据库中的知识发现（KDD）领域的重要研究课题。
- The model of an idealized knowledge discovery system and its several essential components are introduced. 论述了一种理想化的知识发现系统模型，及其各组成部分的功能。
- The grid offers effective support for the computing in the distributed knowledge discovery applications. 网格为分布式知识发现应用中的计算提供了有效支持。
- Data mining, also known as knowledge discovery in database (KDD), is one of the most active fields in database. 数据挖掘又称数据库中的知识发现，是数据库研究最活跃的领域之一。
- KDD (Knowledge discovery in databases) can find out the effective, novel, latent, and apprehensible information. 知识发现(KDD)能够从数据库中识别出有效的、新颖的、潜在有用的、以及最终可理解的信息。
- CHOL has been applied in ethnology and anthropology for Chinese information organization and knowledge discovery. CHOL原型系统已被应用于民族学人类学的信息组织与知识发现。
- Knowledge discovery in databases and data mining aim at semiautomatic tools for analysis of large data sets. 数据库中的知识发现即数据挖掘是致力于大型数据分析中的半自动工具的研究。
- The research will play an important roles in post-processing;realizability and practicality of knowledge discovery. 这项研究对知识发现的后处理与可实现性、实用性起着重要的作用。
- Knowledge discovery in databases, also called data mining, aims at discovery of priviously unknown and potentially useful patterns or knowledge. 在数据库中发现知识，又称为数据发掘，其目标是从大型数据集中发现先前未知的潜在有用的模式或知识。
- For resolving this problem, Knowledge discovery in Data (KDD) and Data-mining Technology are presented to find the information resources. 知识发现和数据挖掘技术就是为迎合这种需要而出现的一种用于开发信息资源的新型数据分析技术。
- Based on ontology services, this thesis proposed an approach to enable end-user to input their knowledge discovery requirements using natural language. 2)、提出了一种基于领域本体服务,用户通过自然语言输入知识发现需求的方案及算法。
- The Bayesian approach is suitable for data mining and knowledge discovery problems characterized by probability and statistics. 因此 ,适用于具有概率统计特征的数据采掘和知识发现问题 ,尤其是样本难得或代价昂贵的问题。
- The KDD (knowledge discovery from database) technology is used to mining the design coefficient to support the model-based design. 由于燃油喷射系统的复杂性，设计模型中存在大量的经验系数。
- M Fayyad,G Piatertsky-Shapiro,P Smyth and R Uthurusamy. Adavance in Knowledge Discovery and Data Mining. AAAI/MIT Press,1996. 魏长华;王淑礼.;知识发现和数据挖掘的研究
