英语释义

• data processing using sophisticated data search capabilities and statistical algorithms to discover patterns and correlations in large preexisting databases; a way to discover new meaning in data

片语

Data Streams Mining数据流挖掘

data-mining model数据挖掘模型

data-mining task数据挖掘的任务

distributed data stream mining分布式数据流挖掘

Data Discovery Mining数据发现和挖掘

data hierarchical mining数据分层挖掘

Data Stream MiningData Stream Mining is the process of extracting knowledge structures from continuous, rapid data record

data-mining agent数据挖掘代理

英汉例句

• 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。

• data mining is the task of finding useful information in large datasets.

  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 process of applying algorithms to data to uncover patterns that match a given context or query.

  数据挖掘是一个这样的过程，即向数据应用算法以揭露匹配给定上下文或查询的模式。

• 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。

• however, for the average user, clustering can be the most useful data mining method you can use.

  不过，对于一般的用户，群集有可能是最为有用的一种数据挖掘方法。

• once you get your data in the instances object, you are free to use whatever data mining you want on the data, so you want this step to be as easy as possible.

  一旦将数据放入了这个实例对象，您就可以在数据上进行任何您想要的数据挖掘了，所以您想要这个步骤尽可能地简单。

• the company uses a form of data mining to group web pages by content, which makes the search engine more efficient, he said.

  他还提到公司使用一种数据挖掘技术来根据内容对网页进行归类，这样一来搜索引擎会更有效率。

• you can express each rule found in the data mining as a merchandising association.

  您可以将在数据挖掘中发现的每条规则表示成商品销售关联。

• this example could quite easily be turned into something more useful, as you have the core for a basic search engine or data mining tool.

  可以很容易地将这个示例推广到一些更有价值的应用场景，因为您掌握了基本搜索引擎或者数据挖掘工具的核心内容。

• the upcoming articles of this series show how to do more powerful things with drill-through definitions, such as invoking data mining dynamically.

  本系列的后续文章将展示如何使用穿透鉆取定义完成一些更复杂事情，比如动态调用数据挖掘。

• clustering denotes a data mining technique that groups data records into clusters of pair-wise similar records by their properties.

  集群是一种数据挖掘技术，这种技术根据数据记录的属性将相近的数据记录指定到集群中。

• large data sets with escalating complexity have pushed traditional data mining techniques to new levels of processing demand.

  复杂性不断提升的大型数据集已经将传统的数据挖掘技术推向新层次的处理要求。

• bi uses techniques such as data mining to extract and identify patterns and correlations in large amounts of data.

  bi利用数据挖掘之类的技术去析取和识别模式，并在大规模数据中进行修正。

• data mining gave us clear indicators, which really helped in creating the right atmosphere in the videos.

  数据挖掘给我们指明方向，很好地帮我们在视频里营造正确的氛围。

• 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。

• in the data mining environment, this means to transfer results to the people who benefit the most from the generated business insights.

  在数据挖掘环境中，这意味着将结果传递给最能从生成的业务洞察力中受益的人。

• there is a growing trend among companies, organizations and individuals alike to gather information through web data mining to utilize that information in their best interest.

  现在在公司、组织、个人中出现了一种通过网络数据挖掘收集信息然后利用那些他们最感兴趣的信息的趋势。

• however, you might want to read this tutorial more than once when you want to translate the data mining techniques into your own business environment.

  但是，如果要将数据挖掘技术应用到您自己的业务环境中的话，那么可能需要反复阅读本教程。

• 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。

• classification (also known as classification trees or decision trees) is a data mining algorithm that creates a step-by-step guide for how to determine the output of a new data instance.

  分类 （也即分类树或决策树）是一种数据挖掘算法，为如何确定一个新的数据实例的输出创建逐步指导。

• 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。

• data mining commonly involves a few standard tasks that include clustering, classification, regression, and associated rule learning.

  数据挖掘通常涉及到一些标準的任务，包括聚集、分类、回归分析和关联性规则学习。