从web挖掘数据需要向通常最多只不过以半结构化格式呈现的信息应用结构。
Mining data from the web involves applying structure to information that is typically presented in a semi-structured format at best.
从当今的输出存储中挖掘数据，要求处理程序试图从通常是完全非结构化的或者最多是半结构化的数据，创建结构化的数据。
Mining data from today's data stores requires that processors attempt to create structured data from data that is often totally unstructured or semi-structured at best.
将半结构化数据表示为基于XML 的文档需要一个健壮的数据挖掘系统来支持XML消费、操纵和输出。
Representing semi-structured data as an XML-based document requires a robust data-mining system to support XML consumption, manipulation, and output.
它还演示了如何组合结构化数据库和文本挖掘。
And it also illustrates how it can be combined with structured databases and data mining.
web上和内部数据存储中非结构化和半结构化数据量的爆炸式增长，促进了对对智能且高效的数据挖掘的需求。
The sheer volume of unstructured and semi-structured data found on the web and in internal data stores increases the need for intelligent and effective data mining.
半结构化数据（主要是HTML形式的）正在开创从web挖掘数据的新局面。
Semi-structured data, primarily in the form of HTML, is enabling new prospects for mining data from the web.
文本挖掘主要处理半结构化、无结构化和字符型数据。
Text mining mainly deals with incomplete data, unstructured data and character data.
论文通过对专家所发表的论文，承担的课题等非结构化数据进行挖掘，来自动判别出专家的知识领域。
In the paper, unstructured data, such as papers published by experts and subjects committed by experts, is mined to recognise the knowledge domain of experts automatically.
本文介绍了数据挖掘、半结构化数据挖掘、XML的相关概念和研究现状，提出了一种面向XML的树型对象模型TOM。
The thesis introduces concepts and present research status about data mining, semi-structured data mining and XML, and produces an oriented-XML treelike object model named TOM.
随着对大量结构化数据分析需求的增长，从图集合中挖掘频繁子图模式已经成为数据挖掘领域的研究热点。
With the increasing demand of massive structured data analysis, mining frequent subgraph patterns from graph datasets has been an attention-deserving field.
阐述了以结构化数据和复杂类型数据挖掘为主要内容的信息挖掘技术。
The information mining technology, which includes the structured data and complex structured data mining, is introduced in this paper.
结论是，电力工程管理数据挖掘必须有一定的数据体系支持，对非结构化数据信息的整理是关键。
The conclusion is that the data mining of power project management should be supported by a certain data system and what how to coordinate the unstructured data information would be the key.
结果：挖掘结构化报告归档数据库中诊断报告的数据资料，形成带有12个节点的疾病知识库决策树，在知识库中存储。
RESULTS: To mine date in archiving database of SR and form the decision tree in disease knowledge base with 12 nodal points, which stored in knowledge base.
针对海量非结构化与半结构化数据进行挖掘分析成为近年来研究的热点。
In recent years, the research about the data mining based on the unstructured and semi-structured data become one of research focuses.
摘要：对于结构化数据的学习是数据挖掘领域一个重要的分支。
Absrtact: Learning structured data is an important branch of the data mining field.
数据挖掘通常是高度结构化的信息应用到大型数据库，以发现新的知识。
Data mining is typically applied to large databases of highly structured information in order to discover new knowledge.
通过约简以减少结构化数据的维数，获得数据集合的不同简洁程度表示已成为数据挖掘的重要任务之一。
Reduction is used to decrease the dimension of structured data and the various compact degrees of data sets are obtained.
本文以标记有序树作为半结构化数据的数据模型，研究了半结构化数据的树状最大频繁模式挖掘问题。
In this paper, labeled ordered tree is used as the data model of semi structured data, the problem of maximum tree structured frequent pattern mining from semi structured data is studied.
传统的数据挖掘方法只能从单一关系中进行模式发现，而很难在复杂的结构化数据中发现复杂的关系模式。
The classical data mining approaches can only look for patterns in single relation, and it is difficult to look for complex relational patterns which involved in multi-relational databases.
传统的数据挖掘方法只能从单一关系中进行模式发现，而很难在复杂的结构化数据中发现复杂的关系模式。
The classical data mining approaches can only look for patterns in single relation, and it is difficult to look for complex relational patterns which involved in multi-relational databases.
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