This article discussed two data mining algorithms: the classification tree and clustering.
本文讨论了两种数据挖掘算法：分类树和群集。
InfoSphere Warehouse contains highly efficient implementations of almost all current data mining algorithms.
InfoSpherewarehouse几乎包含目前所有数据挖掘算法的极为高效的实现。
New data mining algorithms can easily be integrated into this platform if they comply with the data model interface and mining model interface of this platform.
只要遵循该平台的数据模型接口和挖掘模型接口，新的数据挖掘算法可以很容易地集成到该平台中去。
Many stream data mining algorithms have been proposed.
学者们已提出大量处理流数据的挖掘算法。
The ability of data mining algorithms to deal with mass-data becomes more important with the increase in data.
随着数据量的增加，挖掘算法处理海量数据的能力问题日益突出。
Methods Utilization data mining algorithms such as the rough set method to find unknown and potential information.
方法利用粗糙集等数据挖掘的算法，从中发现事先未知、但又是潜在有用的信息。
This article promoted outlier data mining algorithms based on weighted fast clustering to inspect and deal with outlier data effectively.
设计了基于加权快速聚类的异常数据挖掘算法，以便能快速发现异常数据。
Then the characters of stream data mining algorithms are summarized and several techniques that are used in these algorithms are introduced.
然后，总结了流数据挖掘算法的特点，并给出了算法中常用的技术。
However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs.
然而，在将数据挖掘的算法运用到服务器日志上之前，必须对日志数据进行一些预处理。
The models constructed by applying data mining algorithms improve a lot in some aspects, such as efficiency, accuracy, extensibility and adaptability.
应用数据挖掘算法得出的检测模型在检测效率、准确性、可扩展性和自适应性等方面都得到了很大的改进。
The content of this paper includes two respects: research of data mining algorithms and the system structure of the application platform of data mining.
本文的内容包括两个方面：关联规则挖掘算法研究和数据挖掘应用系统体系结构的研究。
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。
The related research aspect includes data warehouse and data mining technologies, construction of CRM systems and design of more effective data mining algorithms.
该领域包括对于数据仓库和数据挖掘技术的研究，CRM系统的构建，以及更加有效挖掘算法的设计等方面。
Compared to the traditional data mining algorithms, the complexity of specific performance of the algorithm in the multi-relational data mining put forward higher requirements.
与传统的数据挖掘算法相比，多关系数据挖掘特有的复杂性对算法的性能提出了更高的要求。
This paper introduces the background, concept and process of data mining technology and data mining algorithms, and elaborates the application actuality of data mining technology.
介绍了数据挖掘技术的背景、概念、流程、数据挖掘算法，并阐述了数据挖掘技术的应用现状。
In the research of data mining-based intrusion detection, data mining algorithms close rely on high standard training datasets, and this limits the validity and generality of results in this field.
在基于数据挖掘的入侵检测研究中往往紧密地依赖于高标准的训练数据集，这严重制约了这一领域研究成果的有效性和通用性。
In the research of data mining-based intrusion detection, data mining algorithms close rely on high standard training datasets, and this limits the validity and generality of results in this field.
在基于数据挖掘的入侵检测研究中往往紧密地依赖于高标准的训练数据集，这严重制约了这一领域研究成果的有效性和通用性。
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