数据挖掘是 in English Translation

数据挖掘是智能数据分析的艺术和科学。

Data mining is the art and science of intelligent data analysis.

数据挖掘是关于分析数据的；.

Data mining is about analyzing data;.

数据挖掘是构建预测模型的必然部分。

Data exploration is an inevitable part of building predictive model.

目前数据挖掘是必要的，主要是由于业务竞争日益激烈。

Data mining is necessary presently mainly due to the growing competition in business.

数据挖掘是通过可视化编程或通过Python脚本完成的。

Data mining is done through visual programming or through Python scripting.

BI和数据挖掘是分开的和相互排斥的。

BI and data mining are separate and mutually exclusive.

统计分析和数据挖掘是Linkedin的顶级技能。

Statistical Analysis and Data Mining are top skills on Linkedin.

数据挖掘是机器学习中的一个研究领域，侧重于通过无监督学习进行探索性数据分析。

Data mining is a field of study in machine learning and focuses on exploratory data analysis through unsupervised learning.

数据挖掘是数据管理和数据预处理的关键过程，因为它确保了数据结构的正确。

Data mining is an integral process for data management as well as the pre-processing of data since it ensures appropriate data structuring.

数据挖掘是基于文本相似性来寻找相似的数据点。

The data exploration is done to find similar data points based on textual similarity.

数据挖掘是分析的基石，可帮助您开发可揭示数百万或数十亿条记录中的联系的模型。

Data mining is a cornerstone of analytics, helping you develop the models that can uncover connections within millions or billions of records.

数据挖掘是其收益已经被证明在不同的领域，企业，政府和非营利组织之间的重要工具。

Data mining is an important tool whose benefits have been demonstrated in diverse fields, among business, government and non-profit organizations.

然而，也有针对数据挖掘是完全不适合作为解决问题。

Yet, there are problems for which data mining is wholly unsuited as a solution.

从技术上讲，数据挖掘是在大型关系数据库中发现数十个领域之间的相关性或模式的过程。

Practically, data mining is the process of finding interrelationship or patterns dozens of fields in large connected databases.

数据挖掘是数据分析的进阶,而人工智能则是商业智能的延伸。

Data mining is the advancement of data analysis, while AI is the extension of business intelligence.

数据挖掘是数据管理和数据预处理的关键过程，因为它确保了数据结构的正确。

Data mining is a key process for data management and pre-processing of data because it ensures proper data structuring.

数据挖掘是高级分析的一个关键方面，它提供的原始数据将被大数据和预测分析所使用。

Data mining is a key aspect of advanced analytics, providing the raw data that will be used by both big data and predictive analytics.

数据挖掘是BI的一个子集，BI由数据挖掘驱动;

Data mining is a subset of BI, BI is driven by data mining;.

要理解当数据挖掘是不适用的，这将是准确界定时，它有帮助的是适用….

To understand when data mining is not applicable, it will be helpful to define precisely when it is applicable.

从技术上讲，数据挖掘是在大型关系数据库中发现数十个领域之间的相关性或模式的过程。

Data mining is technically the process of finding correlations or patterns among dozens of fields in large relational database.

就像数据挖掘是一个过程那样，数据挖掘的定义会包括好几个关于这个过程的解释。

As data mining is a process, the definition will include a number of interpretations of the process.

深度学习在诸如图像识别，语音识别，数据挖掘是导致其增长的主要因素。

The increasing use of deep learning in applications such as image recognition,speech recognition, and data mining is the prime factor resulting in its growth.

例如，我记得不久以前的一次阅读，数据挖掘是商业智能的一个子领域。!

For example, I recall reading,not very long ago, that data mining was a sub-field of business intelligence!

目前尚不清楚的是其他数据挖掘是由谁来完成的，也是为了什么目的。

What's unclear is what other data mining is being accomplished by whom and for what purpose.

数据挖掘是从庞大的数据源中提取有效信息，并将该信息转换为潜在有用且最终易于理解的模式，以供进一步使用。

Data mining is to extract valid information from gigantic data sets and transform the information into potentially useful and ultimately understandable patterns for further use.

这些技术对于数据挖掘是具有实用价值的。

These techniques are useful for data exploration.

我们同意数据挖掘是知识发现过程的一个步骤。

We agree that data mining is a knowledge discovery process.

虽然我们可以很容易地描述这些步骤，但重要的是要意识到数据挖掘是一项敏捷的活动。

Whilst we can easily describe these steps,it is important to be aware that data mining is an agile activity.

虽然数据挖掘是一个比较新的术语，但技术不是。

Although data mining is a relatively new term, the technique is not.