主要介绍网络数据挖掘的基本概念、分类、挖掘的过程及其在电子商务中的应用。
This article mainly refers to the basic concept, classification and process of web data mining, as well as its application in e-commerce.
现在在公司、组织、个人中出现了一种通过网络数据挖掘收集信息然后利用那些他们最感兴趣的信息的趋势。
There is a growing trend among companies, organizations and individuals alike to gather information through web data mining to utilize that information in their best interest.
摘要：文本分类是信息检索和数据挖掘的基础，被广泛应用于网络数据挖掘及搜索引擎等方面。
Absrtact: Text classification is the base of information retrieval and data mining and it is widely used in web data mining and search engine.
语义网络数据挖掘是基于语义网络环境的数据挖掘，它给数据挖掘技术的应用研究提出了新的课题。
Semantic Web data mining is a data mining area based on semantic Web, which introduce new challenges to data mining research.
第一阶段采用神经网络数据挖掘方法，针对企业自身特点和需要，选择一些易得到的统计数据指标选取。
In the first stage, according to the enterprise's own characteristic and need, some apt to received statistics indexes has been chosen to screen to the supplier by taking data mining method.
第三章在算法层次上提出了基于自组织竞争神经网络数据挖掘算法的知识发现系统用于生成拟定的结构方案。
Chapter 3 Applies the KDD system whose data-mining algorithm is based on Self-Organizing Neural Network for generating initial alternative designs.
近年来，随着网络数据挖掘技术的迅猛发展，如何从搜索引擎查询日志中找到有用的信息成为一个重要的研究方向。
During the recent years, with the rapid development of Web data mining, how to find useful information in search engine log query has become an important research direction.
本论文还简要讨论了在数据库中发现知识的数据可视化问题，并采用神经网络技术解决该问题，描述了建立一个神经网络数据挖掘的全过程。
Meanwhile, the paper discusses the problem of data visualization, and resolves it using neural network technique, describes the whole process of building a neural network data mining system.
但是，使用社会网络分析进行更加广泛的数据挖掘会使新的事物成为可能。
But broadening data mining to include analysis of social networks makes new things possible.
在挖掘网络世界一流的可视化数据方面，丘有着无可比拟的敏锐力。
Yau has an unerring ability to unearth the best data visualisations on the web.
关联数据、语义分析、分析数据挖掘，这些都可以作为下一代网络产品和其它附加值的基础。
Linked data, semantic analysis, analytics and data mining all form a layer on top of the content-web that could serve as the foundation for the next series of applications and other added value.
我应该会在2010年毕业。我的研究领域包括：数据库、数据挖掘、安全、社会网络、互联网应用以及数学和其他。
My research areas include, but are not limited to, databases, data mining, security, social networks, Internet applications and some mathematics.
本文在基于数据挖掘的网络入侵检测系统框架基础上设计了一个无导师学习的分析器模型。
Based on the framework of network intrusion detection systems based on data mining, this paper devises an analyzer model of unsupervised learning.
本文介绍一种基于BP神经网络的数据挖掘的分类方法，并提出了改进思想。
This paper presents a classification method for data mining based on BP neural network, and puts forward improvement ideas.
本文主要阐述了数据挖掘技术在工业生产、商业、网络、医药这四个方面的应用，并介绍了一些成功的应用案例。
This article focuses on applications of data mining technology in industrial production, business, networking, medical areas, and introduces some successful application cases.
本文提出了一种基于数据挖掘的网络入侵检测系统模型。
In this paper, a data mining-based network intrusion detection system model is introduced.
利用当前比较先进的自然语言分析技术、全文检索技术、数据挖掘技术等，设计了比较全面的网络课程的智能答疑系统。
Using the present advanced technologies such as nature language, full-text searching and data mining, this paper designed a full-round intelligent question-answer system of network course.
提出了基于数据挖掘的网络入侵检测规则特征值自动发现算法和规则自动生成算法。
This thesis presents data mining aided signature automatic discovery algorithm for network based IDS and detection rule creation algorithm.
本文介绍了数据挖掘技术及其常用方法，并列举了数据挖掘技术在大学物理网络课程中的应用实例。
Data mining and the common methods are introduced, and application of data mining in college physical network course is illustrated.
该文介绍了使用基于数据挖掘和知识发现的神经网络技术来解决库存问题的方法。
This paper introduces neural networks technology based on data mining and knowledge discovery for inventory problems.
数据挖掘和社会网络分析等数据分析技术的兴起，给我们带来了机会。
The rise of the method of Data mining and Social Network Analysis brought an opportunity to us.
贝叶斯网络分类器是数据挖掘与知识发现领域研究的主要方法之一。
Bayesian network classifier is one of the main research methods in data mining and KDD domain.
本文致力于贝叶斯网络的理论和算法的研究，全文研究了如下几个问题：1。贝叶斯网络和数据挖掘的结合。
In this dissertation I dedicate to the research of Bayesian Network's theory and algorithms. The entire thesis can be divided into three parts. 1. Bayesian Networks and Data Mining combine.
简要回顾了各种电力需求预测方法，并针对最近兴起的神经网络方法的不足，提出了用自组织数据挖掘技术进行电力预测。
With a brief review to the prediction methods for electricity demand, the self-organization of data digging is introduced to predict the demand of electricity.
文章探讨了在网络计算的环境下的市场营销离群数据挖掘的重要性与内容。
This paper discusses the importance and approach of marketing outlier mining under the network computing.
其中规则库中包含正常行为规则和异常行为规则，使得原型系统在理论上既可实现误用检测也可实现异常检测，并采用关联规则挖掘模块对网络连接数据进行处理。
The rule sets of the system include normal behavior rules and abnormal behavior rules, it make the system can carry out the anomaly detection and misuse detection in theory.
这种方法的主要思想是利用数据挖掘方法，从经预处理的包含网络连接信息的审计数据中提取能够区分正常和入侵的规则。
The main idea is to apply data mining methods to learn rules that can capture normal and intrusion activities from pre-processed audit data that contain network connection information.
这种方法的主要思想是利用数据挖掘方法，从经预处理的包含网络连接信息的审计数据中提取能够区分正常和入侵的规则。
The main idea is to apply data mining methods to learn rules that can capture normal and intrusion activities from pre-processed audit data that contain network connection information.
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