银行仍然会从你的账户中挖掘数据，以便向你出售金融产品，包括一些没有价值的东西，比如信用保险和信用卡保护计划。
Banks will still be mining data from your account in order to sell you financial products, including things of little value, such as credit insurance and credit-card protection plans.
这些改变使反射可以比以往更深入地挖掘数据结构。
Together, these changes allow reflection to dig deeper into data structures than ever before.
但现在，我们将主要关注使用Hadoop挖掘数据。
在挖掘数据的宽度和广度上，Google没有竞争对手。
They have no rival in the depth and breadth of their data mining.
您会发现要想出色地完成挖掘数据的任务并不像您想象地那么困难。
You will see that it is not as difficult as you might think it is to do a "pretty good" job of mining data.
这就需要挖掘数据来对比过去购车者的年龄和过去购买的车的颜色。
The data can be mined to compare the age of the purchaser of past cars and the colors bought in the past.
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.
经理们掌握其进展情况，通过工具来提供导向使得从工作流中挖掘数据变得容易。
Managers stay abreast of their progress and provide direction by using tools that make it easy to mine data on workflows.
本系列后续的文章将会涉及挖掘数据的其他方法，包括群集、最近的邻居以及分类树。
Future articles will touch upon other methods of mining data, including clustering, Nearest Neighbor, and classification trees.
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.
在实际应用中，为了充分挖掘数据场中包含的信息，还需要对数据进行交互式处理。
In practical applications, in order to fully mine the information contained in data fields, it also needs interactive processing towards data.
结果表明，OLAP用于挖掘数据中的信息是可行的，而且比传统的数据分析方法优越。
The result shows that, to excavate the information from the data using OLAP is feasible, and is superior to the traditional data analysis method.
我们也就能够在我们的服务器上直接挖掘数据，而无须将它处理成一个ARFF文件后才能手动运行它。
This will let us mine the data on our servers directly, without having to manipulate it into an ARFF file or run it by hand.
创建一个分类树(一个决策树)，并借此挖掘数据就可以确定这个人购买一辆新的M5的可能性有多大。
By creating a classification tree (a decision tree), the data can be mined to determine the likelihood of this person to buy a new M5.
量化的安全度量技术在挖掘数据潜在的安全关系和处理大数据量方面比定性方法有明显优势。
The quantification of the security shows obvious superiority over the qualitative ways, especially when it is used to process large datasets and mine the security relations.
它通过为其它公司提供定制式的链接缩短业务，以及从用户创建的链接当中挖掘数据这两种方式赚钱。
It makes money from customised link-shortening services for other firms and from mining the data about links that users create.
人们面临的挑战不再是收集信息，而是挖掘数据以回答特定研究问题——例如人类基因组比果蝇的基因组小如此多的矛盾。
The challenge is no longer collecting information, but mining the data to answer specific research questions - such as the paradox of the human genome being so much smaller than that of a fruit fly.
只有少数公开协定的隐私，36个被访问的设备位置没有事先通知用户，还有5个未经用户许可从用户的地址簿挖掘数据。
Only a small number blatantly compromised privacy: 36 accessed the device's location without first informing the user; another five mined data from the user's address book without permission.
它的设计也充分利用了iPhone的触摸屏，让一些表格视图看起来很简单，而直观的界面可以让用户方便而深入的挖掘数据。
The design makes good use of the phone's touch screen, and while some of the views look deceptively simple, the intuitive interface allows you to easily drill deeper into the data.
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.
数据挖掘技术被用于分析个人的购买习惯。
本文通过向您介绍数据挖掘这个主题的背景以及这个领域的目标力求回答“什么是数据挖掘”这个问题。
This article strives to answer the question "what is data mining?" by giving you a background on the subject and introducing the goals of the field.
什么是数据挖掘？
在这个“用WEKA进行数据挖掘”系列之前的两篇文章中，我介绍了数据挖掘的概念。
In the previous two articles in this "data mining with WEKA" series, I introduced the concept of data mining.
数据挖掘是一项从大型数据集中发现有用信息的任务。
Data mining is the task of finding useful information in large datasets.
数据挖掘是包含许多技术的多学科领域。
Data mining is a multidisciplinary field with many techniques.
数据挖掘有很多实际应用。
数据挖掘过程是怎样的？
通过数据挖掘认识到的益处是，激起了对更有效的数据挖掘技能、技术和解决方案的需求。
The benefits realized through data mining are creating a sharp rise in demand for more effective data mining techniques, technologies, and solutions.
要调用数据挖掘，首先要将数据写到一个表中。
Data mining is invoked by first writing the data into a table.
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