This article discusses the practical space of data mining in digital library and its great value in digital library is also discussed.
在描述数据挖掘技术与方法基础之上，探讨了数据挖掘在数字图书馆中的应用空间以及其所具有的巨大应用价值。
A supervoxel data model is introduced to represent exactly the complex space distribution of geological data in the coal mining area.
首先建立了一种能够有效地表达矿区地质数据复杂空间分布的超体元数据模型；
The search space of Multi-relational data mining algorithm becomes larger and more complex.
多关系数据挖掘算法的搜索空间变得更大、更复杂。
Since aims at small texts data mining, its complexity of time and space is not high. So it can be said this algorithm will become one kind of practical and effective information retrieval technology.
由于是针对小文本的数据挖掘，本文研究的算法时间和空间复杂度都不高，因此有望成为一种实用、有效的信息检索技术。
By reduction processing to the import space, this method adopts artificial neural network for data mining on the reduced training data.
通过属性约简技术对神经网络的输入属性空间进行约简，采用神经网络对约简后的数据进行挖掘。
For more effective meteorological data mining, this thesis introduces the quotient space granular computing theory, grey model, structural machine learning algorithm and so on.
为了更加有效地进行瓦斯数据挖掘，本文引入了商空间粒度计算理论、灰色模型、覆盖算法等。
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.
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。
Thus, it is important to research data stream mining algorithms having higher time and space efficiency, and to aim at resolving data mining tasks often used in system simulation.
因此，针对仿真中常用的数据挖掘任务，研究时空效率高效的相应数据流挖掘算法具有重要意义。
Missing values in traffic flow data should be imputed because complete data are needed for space-time data mining.
交通流量的时空数据挖掘需要完整的数据，因此必须处理交通流量数据中的缺失值。
Traditional data mining algorithms aiming at static datasets can't be used to mine data streams directly, neither do they have the time and space efficiency.
传统面向静态数据集的算法无法直接用于挖掘数据流，而现有数据流挖掘算法存在时空效率不高的缺陷。
Traditional data mining algorithms aiming at static datasets can't be used to mine data streams directly, neither do they have the time and space efficiency.
传统面向静态数据集的算法无法直接用于挖掘数据流，而现有数据流挖掘算法存在时空效率不高的缺陷。
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