Now, a University of Utah computer scientist has devised a new method for simpler, faster "data mining," or extracting and analyzing massive amounts of such data.
现在，犹他大学的计算机科学家设计出了一种新的方法，能简单快速地实施“数据挖掘”或提取与分析巨量数据。
Data mining technology can find useful information for decision-making from massive history data thus it has been applied widely.
数据挖掘技术能从海量数据中，发现对决策有重要作用的信息，因而得到广泛应用。
Data mining, a new branch of study emerged recently, concentrates primarily on how to identify useful knowledge from massive data.
数据挖掘技术是一门近年来新兴的学科，它主要研究如何从大量数据中发掘出有用的知识。
Data Mining is a new technology to analyze the association rules among massive data.
数据挖掘技术是当今研究大量数据间的关联规则的一种新技术。
But, if we need recalculate rules from all data to mine knowledge after updating the database, it will consume massive resources, which causes the urgent demand of the incremental mining algorithm.
但是，如果在数据库更新之后要对全部数据重新进行挖掘，需要消耗大量的资源，这导致对增量挖掘算法的迫切需求。
Facing the massive volume and high dimensional data, how to build effective and scalable algorithm for data mining is one of research directions of data mining.
面对大规模、高维的数据，如何建立有效的，可扩展的分类数据挖掘算法是数据挖掘研究的重要方向之一。
Facing the massive volume and high dimensional data how to build effective and scalable clustering algorithm for data mining is one of research directions of data mining.
面对大规模的、高维的数据，如何建立有效、可扩展的的聚类数据挖掘算法是数据挖掘领域的一个研究热点。
With the increasing demand of massive structured data analysis, mining frequent subgraph patterns from graph datasets has been an attention-deserving field.
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。
Data mining summarizes the knowledge based on the massive information in data warehouse.
而数据挖掘可以从数据仓库的海量信息中归纳出知识。
Association rules can find some correlation info between these fields. By use of statistic techniques of data mining we can draw exact and plain statistics reporting from the chaotic and massive data.
关联规则能发现数据中的属性字段之间的相关性，利用数据挖掘中的统计技术我们可以从纷繁杂乱的海量数据中得出条理清晰的统计数据报表。
In addition, the business data stream is continuous, conflict, timing, massive and distributed, so traditional data mining techniques can not be applied directly to the business data stream.
而商业数据流除了具备数据流的基本特点外，还具备连续性、冲突性、时间性、海量性和分布性等特性。因此传统的数据挖掘技术不能直接应用到商业数据流上。
The potential and useful information in massive disordered data can be acquired through data mining.
通过数据挖掘技术可以得到看似杂乱的数据间存在的潜在的、有用的信息。
Additionally, machine learning methods are also applied to massive-sample data mining field.
除此之外，机器学习的理论方法还被用于大数据集的数据挖掘领域。
Additionally, machine learning methods are also applied to massive-sample data mining field.
除此之外，机器学习的理论方法还被用于大数据集的数据挖掘领域。
应用推荐