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科学数据挖掘
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  scientific data mining
     This thesis focuses on the application of neural network prediction theory in scientific data mining from three aspects: theory, algorithm and application.
     本文从理论、算法及应用三个层面讨论了神经网络预测理论在科学数据挖掘中的应用。
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     The experiment results show that the algorithm is very efficient in the scientific data mining.
     实验证明了这种算法在科学数据挖掘中是很有效的。
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     Authenticated Dictionary(AD)is one of the important data structures,and it is of great theoretic and applicable value in many research fields,including scientific data mining,geographic data servers,third -party data publication on the Internet and certificate revocation in public key infrastructure.
     认证字典是一类重要的数据结构,它在众多研究领域都具有重要的理论和应用价值,诸如科学数据挖掘、地理数据服务器、Internet上的第三方数据发布以及PKI中的证书撤销等。
短句来源
     An improved clustering algorithm applied to scientific data mining
     一种改进型聚类算法应用于科学数据挖掘
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     (1) Based on detailed analyses of the data mining properties of the Scientific Data Grid, ascientific data mining system is proposed. The system consists of three main components: theScientific Data Mining Architecture (SDMA), the Scientific Data Mining Toolkit (SDMK), andthe Scientific Data Mining Service (SDMS).
     本文的主要研究内容和创新成果包括:(1)科学数据网格环境下的科学数据挖掘系统本文在分析科学数据网格环境下数据挖掘之特点的基础上,提出了网格环境下的数据挖掘解决方案——科学数据挖掘系统。
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  science data mining
     The main point of this project is to research the theories and applications of artificial neural network(ANN) which is suitable for large scale science data mining.
     本项目主要研究适合于大规模科学数据挖掘的神经网络理论和应用。
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     This thesis aims to discuss the clustering techniques with the background of large-scale nuclear physics Science Data Mining. First, we introduce the key techniques and the main task in Data Mining, then we analyze the data preprocessing techniques and clustering techniques combine Data Mining techniques with Science Data.
     论文基于大规模核物理科学数据挖掘的背景,全面介绍了数据挖掘的关键技术和主要任务,从理论、算法和应用三个层次,结合科学数据的特点来分析预处理技术和聚类方法,提出了很多实用的预处理方法:对HDF5科学数据进行分块、除噪、集成、变换等,同时对它使用“截断法”和“逐层求差法”进行规约,并对数据进行信息提取。
短句来源
     The main point of this paper is to research the theories and applications of classifying and clustering which is suitable for large-scale science data mining.
     本文主要研究适合于大规模科学数据挖掘的分类和聚类的理论和应用.
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  “科学数据挖掘”译为未确定词的双语例句
     SDMA describes the multi-dimension modelarchitecture of data mining applications;
     该系统主要由三部分构成:科学数据挖掘系统结构描述了数据挖掘程序中基于多维模型的三层结构;
短句来源
     SDMK provides a large amount of data preprocessingand data mining algorithms;
     科学数据挖掘工具集提供了大量的数据预处理算法和数据挖掘算法;
短句来源
     SDMS presents a data mining scheme to address the problemsunder grid environment through a form of grid service.
     科学数据挖掘网格服务以网格服务的形式提供了科学数据网格环境下的数据挖掘解决方案。
短句来源
     Compared with traditional data miningsystems, the proposed system has many excellent properties, and is more suitable to theenvironment of the Scientific Data Grid and the Scientific Database.
     与传统的数据挖掘系统相比,科学数据挖掘系统具有诸多优异的特点,更为适合科学数据网格和科学数据库环境。
短句来源
     Nowadays, it has beenapplied in some real database applications.
     目前,科学数据挖掘系统已经实际应用于几个数据库中。
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  scientific data mining
In this paper, the post-processing of midwater multibeam backscatter data is placed in a scientific data mining framework.
      
When the data being mined is of a scientific nature, the term scientific data mining is commonly employed.
      


Authenticated Dictionary(AD)is one of the important data structures,and it is of great theoretic and applicable value in many research fields,including scientific data mining,geographic data servers,third -party data publication on the Internet and certificate revocation in public key infrastructure.Basic concept and principle of AD are examined.Time constraint is introduced into the model of AD for the first time ,and a new taxonomy of AD is presented.Afterwards,implementation technologies of AD are given.Finally,applications...

Authenticated Dictionary(AD)is one of the important data structures,and it is of great theoretic and applicable value in many research fields,including scientific data mining,geographic data servers,third -party data publication on the Internet and certificate revocation in public key infrastructure.Basic concept and principle of AD are examined.Time constraint is introduced into the model of AD for the first time ,and a new taxonomy of AD is presented.Afterwards,implementation technologies of AD are given.Finally,applications of AD in PKI /WPKI fields are briefly described.

认证字典是一类重要的数据结构,它在众多研究领域都具有重要的理论和应用价值,诸如科学数据挖掘、地理数据服务器、Internet上的第三方数据发布以及PKI中的证书撤销等。该文介绍了认证字典的基本概念与原理;首次在认证字典模型中引入了时间约束,并据此给出了认证字典的一种新的分类方法,探讨了认证字典的实现技术。最后,简要讨论了认证字典在PKI/WPKI中的应用。

Independent Component Analysis (ICA)is a linear transformation based on high-order statistic properties of the sample data. It has been widely used for image processing and Blind Source Separation (BSS). This paper introduces ICA to the field of scientific data mining and proposes a framework of ICA feature extraction. Furthermore, we give an efficient algorithm—FastICA and explore the application of proposed framework on high-dimensional scientific data. Experiments show that ICA is suitable for feature extraction...

Independent Component Analysis (ICA)is a linear transformation based on high-order statistic properties of the sample data. It has been widely used for image processing and Blind Source Separation (BSS). This paper introduces ICA to the field of scientific data mining and proposes a framework of ICA feature extraction. Furthermore, we give an efficient algorithm—FastICA and explore the application of proposed framework on high-dimensional scientific data. Experiments show that ICA is suitable for feature extraction and performs better with higher accuracy than other traditional ways in the field of scientific data mining.

独立分量分析(ICA)是基于数据高阶统计特性的一种线性变换手段。目前,已广泛应用于盲信号分离和图像识别。文章将此技术引入到科学数据挖掘领域,以求解决预处理中高维复杂特征的提取问题。提出了ICA结合主成分分析(PCA)的特征提取步骤,并结合科学数据集量大的特点给出了一种快速收敛算法—FastICA。最后指出ICA特征提取技术可以应用于高维科学数据挖掘,并且较传统的特征提取技术有更高的准确率。

The clustering is an important part of the scientific data mining.Among the various algorithms a main class is based on the "distance".The "K-means" and "k-medoids" are two of these kinds.However these algorithms are inefficient when dealing with the large data sets and data sets of high-dimension.This algorithm differs much from the above ones and it takes a totally different approach called a grid and density based algorithm.It can automatically find out the subspaces containing interesting patterns and discover...

The clustering is an important part of the scientific data mining.Among the various algorithms a main class is based on the "distance".The "K-means" and "k-medoids" are two of these kinds.However these algorithms are inefficient when dealing with the large data sets and data sets of high-dimension.This algorithm differs much from the above ones and it takes a totally different approach called a grid and density based algorithm.It can automatically find out the subspaces containing interesting patterns and discover all clusters in that subspace and it performs well when dealing with the high-dimensional data.

聚类是科学数据挖掘中的核心问题。在已提出的聚类算法中大都是基于“距离”的概念,这类算法的缺点在于处理数据量大和维数高的科学数据时不够有效,因此提出迭代网格算法。这个算法与基于距离的损法有根本不同,它抛弃了距离的概念,而采取一种新的思路。它不仅能够自动发现包含有趣知识的子空间,并将里面存在的所有聚类挖掘出来;而且它能很好的处理维数高和数据量大的科学数据。

 
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