Then, see how to invoke data mining dynamically from Cognos.
然后，再来看看如何从Cognos动态调用数据挖掘。
They have no rival in the depth and breadth of their data mining.
在挖掘数据的宽度和广度上，Google没有竞争对手。
Could data mining soon replace serendipitous observational discovery?
未来，数据挖掘是否会代替偶然观测的发现？
XML-based dialects are used quite extensively for data mining purposes.
XML的方言相当广泛地用于数据挖掘目的。
Can also be used to log messages for audit or for subsequent data mining.
还可以用于将消息记入日志，以供审核和后续数据挖掘之用。
This data must be collected and transformed to enable it for data mining.
这种数据必须在收集后进行转换，使之能用于数据挖掘。
The first issue with data mining of distributed data sources is discovery.
分布式数据源数据挖掘的第一个问题是发现。
It's much easier to run the data mining model than to load the data into it.
运行数据挖掘模型要比将数据载入模型简单得多。
We're not talking about Twitter clients, we're talking about Twitter data mining.
我这里谈论的不是Twitter客户端，而是Twitter数据挖掘。
How can the results of data mining be delivered to analysts and employees who need it?
如何将数据挖掘的结果交付给需要它的分析师和雇员？
Consume data mining results in Cognos reports: An example from the health care sector.
在Cognos报告中使用数据挖掘结果：卫生保健部门的一个例子。
All data mining functions are invoked just as usual SQL commands, as you will see below.
所有数据挖掘函数都是作为常规的SQL命令来调用的，后面您将看到这一点。
The dealership wants to increase future sales and employ data mining to accomplish this.
这个经销店想要增加未来的销售并部署了数据挖掘来实现此目标。
You also looked briefly at existing standards for using XML in the context of data mining.
还简单了解了关于在数据挖掘上下文中使用XML的现有标准。
DB2 UDB ESE with DPF is ideal to manage OLAP, data warehousing, and data mining workloads.
具有DPF功能的DB 2UDBESE是管理OLAP、数据仓库和数据挖掘工作负载的理想选择。
Ethnographic field studies, focus groups, diary studies, surveys, data mining or analytics.
人种学现场研究，焦点小组，日记研究，调查问卷，数据发掘和分析。
But broadening data mining to include analysis of social networks makes new things possible.
但是，使用社会网络分析进行更加广泛的数据挖掘会使新的事物成为可能。
For additional ways to increase your knowledge about data mining, plan to check the Resources.
关于增加您的数据挖掘知识的其他方法，请参阅参考资料。
You can add reports that take advantage of all the data mining techniques we've learned so far.
您可以添加充分利用了我们到目前所学的全部数据挖掘技术的报告。
The challenge of data mining is dealing with the dimensionality of the data and the volume of it.
数据挖掘所面临的挑战在于数据的维数及其容量。
That may leave you with the impression that data mining is something only big companies can afford.
这会为您留下一种印象，即只有大公司才能负担得起数据挖掘。
This last article will discuss the last of the four common data mining techniques: Nearest Neighbor.
本文将讨论四种常用的数据挖掘技术中的最后一种：最近邻。
The record-breaking MapReduce approach, he said, is useful in physics, cryptography and data mining.
他还介绍说，破记录的MapReduce方法在物理学，密码学和数据挖掘领域也是很有用的。
The resulting XML data mining model is stored in the database and can be accessed through SQL/XQuery.
这样得到的XML数据挖掘模型存储在数据库中，可以通过 SQL/XQuery进行访问。
This type of data mining is the focus of this article, specifically how to collect and parse this data.
这种类型的数据挖掘是本文的焦点，本文将专门介绍如何收集和解析这种数据。
Large, complex data sets escalate traditional data mining techniques to new levels of processing demand.
大型、复杂的数据集将传统的数据挖掘技术推向新层次的处理要求。
Remember, you are never going to be an expert in data mining unless you want to spend 20 years doing it.
请记住，您永远不可能成为数据挖掘方面的专家，除非您打算用20年的时间来研究它。
The results of data mining, on the other hand, are often relevant to different users across the company.
另一方面，数据挖掘的结果又常常与公司中各种不同的用户有关联。
InfoSphere Warehouse contains highly efficient implementations of almost all current data mining algorithms.
InfoSpherewarehouse几乎包含目前所有数据挖掘算法的极为高效的实现。
In the previous two articles in this "data mining with WEKA" series, I introduced the concept of data mining.
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。
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