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。
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。
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。
Of all the incremental mining algorithms, the IUS is the most advanced at present.
在序列模式的增量式挖掘算法中，IUS算法是目前最为先进的算法。
Comparing with presented top-down mining algorithms, the experiments indicate that it is fast and efficient.
实验证明，与现有的自顶向下挖掘算法相比，该算法是快速而有效的。
According to the analysis of association rules mining algorithms, the CPH and AOAA algorithms are presented.
根据对关联规则挖掘算法的分析，提出了CPH算法和AOAA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。
Experiments show great performance gains over existing sequential pattern mining algorithms, especially for large database.
实验证明，该算法在大规模数据的处理上比现有序列模式挖掘算法有更好的性能。
Frequent item mining algorithms need to perform as little data stream scanning as possible while using limited size of memory.
数据流频繁项挖掘算法需要利用有限的内存，以尽量少的次数扫描数据流就能得到频繁项。
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。
The existing association rules mining algorithms are chiefly based on frequent itemsets, and the record about infrequent itemsets is very rare.
现有关联规则挖掘算法都是在频繁项集基础上进行挖掘，关于非频繁项集的资料很少。
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。
It includes preparation and selection of data, preprocessing, selection and implementation of mining algorithms, postprocessing of mining results, etc.
它包括数据的准备与选择、数据的预处理、挖掘算法的选择与实现、挖掘结果的后处理等步骤。
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。
However, we found it is hard to select the appropriate support threshold for datasets with high skewed support distribution by the current mining algorithms.
对支持度分布严重倾斜的数据集挖掘时，人们发现现有算法无法选择合适的支持度阈值。
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。
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。
By far, association rule mining algorithms can be divided into two main classes: width first and depth first. There are classical and efficient algorithms in each class.
目前关联规则挖掘算法可以分为广度优先算法和深度优先算法两大类，每类都有经典高效的算法提出。
Based on comment of DSMS model, various frequent pattern mining algorithms are analyzed thoroughly and their characteristics and limitation are pointed out in this paper.
在论述数据流管理系统模型的基础上，深入分析了国内外的各种频繁模式挖掘算法，并指出这些算法的特点及其局限性。
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。
This paper introduces the concepts of original association rules and proves all rules in the rules set mined by conventional mining algorithms can be generated by the original association rules.
该文介绍了原关联规则的概念，证明了传统算法挖掘出的关联规则集中的任何规则，均可以由原关联规则导出，而原关联规则的数目远远小于传统算法挖掘出的关联规则数目。
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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