大规模数据集是数据挖掘高效实现的障碍。
Large data sets are becoming obstacles for efficient data mining.
集群存储是解决大规模数据存储的重要方法。
Cluster storage is an important solution to large-scale data storage systems.
因此对大规模数据的整合是当前面临的挑战。
The structured deep Web thus presents challenges for large- scale information integration.
算法执行效率高，适合大规模数据的聚类问题。
The algorithm is efficient and can facilitate the clustering of a very large datas...
BIRCH算法是针对大规模数据集的聚类算法。
BIRCH algorithm is a clustering algorithm for very large datasets.
这种新业务模式为拥有大规模数据的公司提供了新的商业机会。
The new business model will create new business opportunities for companies owning large amounts of data.
对于大规模数据转移，要仔细地制定计划并考虑作业的自动化。
For large movement of data, it is much more about planning, discipline and the ability to automate jobs.
CURE算法是针对大规模数据聚类算法的典型代表。
CURE is a typical clustering algorithm that is designed for the mining of mass data.
它是凭借强大的处理能力和大规模数据存储能力获胜的。
It won by dint of sheer processing power and massive data storage capability.
因而，大规模数据集的交互渲染不能通过强力模型进行。
As a result, massive datasets cannot be interactively rendered by brute force methods.
但是面对大规模数据，科学家“假设、模型、检验”的方法变得过时了。
But faced with massive data, this approach to science - hypothesize, model, test - is becoming obsolete.
但是面对大规模数据，科学家“假设、模型、检验”的方法变得过时了。
But faced with massive data, this approach to science -hypothesize, model, test -is becoming obsolete.
可伸缩性：系统在运行时能够处理大规模数据和计算的程度。
Scalability: the degree to which the system can cope with large amount of data and computation at runtime.
Gnip和DataSift都已经建立可靠网络用以对付实时的大规模数据。
Both Gnip and DataSift have built robust networks which can cope with massive amounts of data in real time.
应该认识到 R是一个可以管理、总结和利用大规模数据的有效而灵活的工具。
One way to think about R is that it's an efficient, flexible tool for managing, summarizing, and exploiting large-scale 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。
BI USES techniques such as data mining to extract and identify patterns and correlations in large amounts of data.
并发集合让大规模数据集的管理更加简单，并可以大量减少使用同步的需要。
Concurrent collections make it easier to manage large collections of data, and can greatly reduce the need for synchronization.
数据挖掘解决了传统分析方法的不足，并能够对大规模数据的进行分析处理。
Data mining solve the inadequacy of traditional analysis methods, and it can conduct large-scale data analysis and processing.
这些黑客才是各公司所承认的一些大规模数据泄露事件的幕后黑手（如图表所示）。
These gangs are behind some of the biggest data breaches that companies have owned up to (see chart).
该协议由紧急上报和兴趣命令驱动数据传输，适用于大规模数据采集与环境监测。
It drives data transportation through exigency report and interest demand, and can be used in large scale data collecting and environmental monitoring.
基于空间点集的连通性构造的等价关系，提出一种针对大规模数据集的快速分组算法。
Based on the equivalent relationship constructed from the connectivity, a fast grouping algorithm is presented for mass data set.
如何提高副本定位服务的可扩展性和自适应性，是大规模数据网格系统面临的重要难题。
It's a challenging problem to improve the scalability, adaptability and performance of replica location service in large-scale data grids.
利用技术公司提供的大规模数据组，微观经济学家能够对人们的行为作出异常准确预测。
Armed with vast data sets produced by tech firms, microeconomists can produce startlingly good forecasts of human behaviour.
比较分析技术确定一组大规模数据结构，它们在许多组成数据类型的实例数目方面快速增长。
The comparative analysis technique identifies a set of large sized data structures experiencing significant growth in the number of instances of constituent data types.
该方法尤其适用于大规模数据的模糊聚类分析，对于模糊聚类分析的推广使用有重要意义。
It fits for fuzzy clustering for a large amount of data especially, which helps greatly to use fuzzy clustering more extensively.
本科课程适合智能信息处理、模式识别、大规模数据挖掘、生物信息学等专业的硕士研究生。
The course is suitable for the master degree students working on intelligent information processing, pattern recognition, data mining and bioinformatics.
生物学数据点的收集需要描述细胞结构和事件的小、中、大规模数据的从简单到复杂的分析。
Collection of biological data points require simple to complex analysis of small, medium and large scale data describing cell structures and events.
生物学数据点的收集需要描述细胞结构和事件的小、中、大规模数据的从简单到复杂的分析。
Collection of biological data points require simple to complex analysis of small, medium and large scale data describing cell structures and events.
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