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- A new method is proposed for sample selection in large data set. 提出了一种大规模数据集的训练样本选择方法。
- The main problem of IBR is the organization method for large data set, and the cooperation strategy with geometry model. 基于图像的绘制技术中的关键问题是采样数据的组织模式，及其与场景几何模型的结合策略。
- Support distributed computation models to process large data sets. 支持分布式计算模型,处理大规模数据。
- By contraries, the algorithm proposed could get rid of the constraints.Moreover, besides supplying some interesting rules to user, it also does well in mining for large data set. 而本文提出的方法不再受到上述限制的困扰，并且可以挖掘出用户感兴趣的规则，尤其对于大规模样本集的效果也是相当不错的。
- LPI is optimal in the sense of local manifold structure.However, LPI is not efficient in time and memory, which makes it difficult to be applied to very large data set. 摘要LPI对于局部流形结构是优化的，但在时空上运行效率较低，使其很难应用于大型数据集。
- Abstract LPI is optimal in the sense of local manifold structure.However, LPI is not efficient in time and memory, which makes it difficult to be applied to very large data set. 摘要LPI对于局部流形结构是优化的，但在时空上运行效率较低，使其很难应用于大型数据集。
- Knowledge discovery in databases is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in large data set. 数据库中的知识发现是指在大型数据集中识别有效、新奇、潜在有用、且最终可理解模式的非平凡的过程。
- UDDI is not designed to support large data sets required by some research uses. UDDI并不是为那些通过搜索获取大量数据集的使用场合而设计的。
- The comparing experiment shows that the performance of ALCM method is higher than the PAM with large data set,and it is not obviously different between two methods about the value of Cost function. 通过比较实验表明:1)随着数据个数的增大;PAM所花费的时间将激剧增大;而ALCM花费时间与数据集个数呈近似线性增长的关系;即ALCM是适应大数据集的.;2)PAM算法和ALCM算法随数据个数增大;二者的代价函数并无明显差异
- But the PAM can work well with large data set. To solve the problem,this paper shows an Approximated Linear Clustering Method(ALCM),and proves that the complexity of the new algorithm is O(n),where n is the number of data set. 本文针对PAM算法不适应大数据集的缺点;给出一个近似的线性时间聚类算法(ALCM);并且从理论上证明了该算法复杂度为关于数据集个数的线性时间复杂度.
- If you are working with large data sets, consider using a dedicated buffer pool for the table space. 如果是使用大型数据集,可考虑用专用的缓冲池来代替表空间。
- A larger data set, one that could not be stored entirely in memory easily, would have required the entire application to be built around a database. 更大的数据集,比如不能完全存储到内存中的数据集,会要求整个应用程序都围绕着一个数据库构建。
- In order to improve the efficiency we propose a distributed clustering algorithm based on large data sets. 为了提高聚类效率提出了一种基于分布式的大数据集聚类算法。
- However, for very large data sets with many dimensions, MOLAP solutions aren't always so effective. 然而，对于非常大的多维数据集， MOLAP方案并不总是有效的。
- Knowledge discovery in databases and data mining aim at semiautomatic tools for analysis of large data sets. 数据库中的知识发现即数据挖掘是致力于大型数据分析中的半自动工具的研究。
- The largest data set consisted of 12847 records with 47 sires and 778 HYSs, which is corresponding to the milk yield data available currently in Beijing area. 4种数据结构中最大的有12847个观察值, 场年季效应和公牛效应水平数分别为778和47,它与北京市目前可利用的奶牛头胎产奶量记录资料相当。
- When working with large data sets, this analysis can negatively impact the performance of the control when automatic resizing occurs. 当处理大数据集时,如果发生自动大小调整,这种分析可使控件的性能下降。
- We conclude that cache-oblivious algorithms do outperform traditional RAM-model algorithms when working on large data sets. 本文的主要结论是，当处理大数据量时，高速缓存参数无关算法显著优于传统的基于ram模型的算法。
- Meetings address topics including biogeography, systematics, visualization of large data sets, conservation, evolution, and biodiversity hotspots. 这些研讨会的主题包含生物地理学、系统分类学、视觉化资料库、生态保育、演化与生物多样性热点确立。
- Ramaswamy, R Rastogi, K Shim. Efficient algo rithms for mining outliers from large data sets [A]. In:ACM SIGMOD Conference Proceedings [C], New Or leans: 2000. 周海燕.;空间数据挖掘的研究[D]
