现实挖掘

前卫的现实网络挖掘

Web Mining On Reality

针对目前参政议政存在的问题，本文力求结合现实，挖掘新材料，并力求找出问题存在的深层次原因。

Bur there are some problems in political participation , I try to excavate new materials , and sought to identify the problem of deep-seated reasons .

二是施暴者通过网络，把网络中人物的现实身份挖掘出来，通过大量信息检索曝光当事人各种个人信息，从而对当事人的实际生活造成干扰。

The second is through the violent network , the network of the characters in real identity " mining " come out , through a lot of information retrieval exposure parties all sorts of personal information , so as to the real life cause interference .

关联规则挖掘是从大量的、不准确的、嘈杂的现实数据中挖掘出隐含在其中的、潜在的有价值的相关信息和知识的过程。

Association analysis method is getting useful information from these massive , inaccurate , noisy data .

在许多现实的数据挖掘应用中，排列也非常重要。

In many real-world data mining applications , a ranking of test instances also is very important .

理性地审视当代社会主义与资本主义发展的现实，深入挖掘马克思恩格斯思想的丰富内涵，对于建设中国特色社会主义具有重要的现实意义。

Therefore , the reasonable analysis of the socialist and capitalist development in modern world and the thorough understanding of the connotation of Marxist and Engles ' philosophy are of great significance to the construction of socialism with Chinese characteristics .

基于虚拟现实技术的数据挖掘结果可视化

Visualization for the Result of Data Mining Based on Virtual Reality

企业针对产品同质化严重的现实情况，深入挖掘市场潜力，树立品牌形象，加强顾客的购买意愿。

Face the serious realities of product homogeneity , the enterprise in-depth digs the market potential and expand the brand influence and achieve customer buying .

并从历史及现实两个层面挖掘了中国社会诚信流失的根源在于社会历史原因和传统计划经济体制的束缚；

The reasons why credit-cracking exists in Chinese society were attributed to two aspects : accumulation through history and the bondage of a traditionally-planned economy system .

本文报告了数据挖掘分析的现状，通过一个现实的航天数据挖掘分析的项目构架提出当今数据挖掘领域所普遍存在并没有被注意到的一些问题。

This article describes the actuality of the data-mining and analysis , and points out some problems that appear in the data-mining field through a space data-mining and analysis project .

现实世界的数据挖掘应用经常牵涉到巨大的数据集，因此，对巨大的数据集进行多次扫描或者把数据集所有实例都加载到内存中来做谱聚类都是不可行的。

Real-world data mining applications often involve huge data sets , which are placed in memory to do spectral clustering is not feasible . Many real-world data mining applications involve millions or billions of data records where even multiple scans of the entire data are too expensive to perform .