在医院现有信息管理系统基础上，建立数据仓库，利用数据挖掘可视化技术，对海量历史数据进行组织和管理，用来支持管理决策。
Dataware is builded based on his. The visual data mining is applied to organize and manage a large amount of historical data and aimed to support management and decision-making.
在挖掘网络世界一流的可视化数据方面，丘有着无可比拟的敏锐力。
Yau has an unerring ability to unearth the best data visualisations on the web.
这种图形符号的语义联想方法可以用来改善信息检索系统的人机交互效率以及用于数据挖掘领域中的信息可视化技术。
The method of icon semantics association can be applied to the improving of the information retrieve system based on icon, and can be applied to information visualization technology in data mining.
第五章，基于可视化数据挖掘的结果分析，主要包括二松水质状况的空间分布规律以及影响因子分析。
The fifth chapter analyzesthe results based on visualized data mining, about spatial distribution discipline of SecondSonghua River water quality and it's affecting factors.
这两者的结合便产生了可视化的数据挖掘技术。
The combination of them produced the technology of visual data mining.
常用的数据挖掘方法包括描述、分类、关联规则、聚类、孤立点检测、模式匹配、数据可视化等。
Several major kinds of data mining methods, including characterization, classification, association rule, clustering, outlier detection, pattern matching, data visualization, and so on.
SPMI的核心技术是可视化技术和数据挖掘技术。
该方法是以数据挖掘技术解决数据的选取、分析和预测，以数据可视化技术实现数据的表现。
The method uses DM technology to select, to analyze and to predict data, and uses data visualization technology to show data chart.
本论文还简要讨论了在数据库中发现知识的数据可视化问题，并采用神经网络技术解决该问题，描述了建立一个神经网络数据挖掘的全过程。
Meanwhile, the paper discusses the problem of data visualization, and resolves it using neural network technique, describes the whole process of building a neural network data mining system.
通过研究数据挖掘中的各种可视化方法，提出了一种新颖的交互式可视化例外数据挖掘方法。
Through researching various visualization methods on data mining, we propose a novel interactive visualization outlier data mining method.
扩展后的DM 2平台具有极强的处理大数据集的能力，优异的与数据库交互的能力，人性化的可视化数据挖掘界面。
The expanded DM2 platform has extremely capacity to handle large data sets, excellent ability to interact with database and human visual interfaces of data mining.
并讨论了空间数据可视化表达模式和可视化与空间数据挖掘的整合。
Visualization mode of spatial data and integration of spatial data mining and visualization are discussed.
基于可视化数据挖掘的任务和目标，阐述了可视化数据挖掘技术的发展趋势。
Trends are clarified based on the task and object of Visual Data Mining.
数据挖掘技术起源于从统计方法，模式识别，数据库，人工智能，高性能和并行计算和可视化。
Data mining techniques have their origins in methods from statistics, pattern recognition, databases, artificial intelligence, high performance and parallel computing and visualization.
数据挖掘和数据可视化技术的结合形成可视数据挖掘。
Data mining combines with data visualization to form visual data mining.
这里，我们描述了促进这些数据的有效挖掘和可视化的最新数据库进展。
Here, we describe recent database developments that facilitate effective mining and visualization of these data.
通过可视化技术的运用，数据挖掘可以增强其数据的针对性和结果的可信度。
By applying visualization technique, data mining can strengthen the aim of the data and the reliability of the result.
通过可视化技术的运用，数据挖掘可以增强其数据的针对性和结果的可信度。
By applying visualization technique, data mining can strengthen the aim of the data and the reliability of the result.
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