基于贝叶斯分类的选股方法

本文使用“事件研究”方法分析了证券分析师推荐股票的总体特征,试图找出符合这些特征的股票而获得超额回报,并应用基本贝叶斯分类方法进行选股。经对上证A股的所选股票的收益率统计分析,通过合理地选取贝叶斯分类器参数可以获得较好回报。结果表明了这种方法是有实际意义和效果的。     

数据挖掘技术研究综述

随着信息技术的发展,积累了越来越多的数据。数据挖掘技术通过从数据库中发现知识,为人类处理这些海量数据提供了有力武器。首先介绍了数据挖掘技术的概念,然后对数据挖掘系统的构成和数据挖掘的流程进行了分析,最后详细分析了数据挖掘的常用方法。     

基于数据挖掘的移动通信业客户流失预警系统设计

借助SASEM平台。对移动通信业务数据使用数据挖掘算法建立客户细分模型,能够刻画移动通信客户的行为特征,并以此建立客户流失预测模型。从而建立一个移动通信业客户流失预警系统。实践证明,该方法实用、可操作性强,对支持企业客户关系管理产生了积极的影响。     

基于数据挖掘技术的入侵检测系统框架

入侵检测系统是近年来出现的一种网络安全技术,在网络安全领域中发挥着越来越大的作用。但入侵检测系统自身的误报漏报及海量信息的涌现,使得人们必须谋求突破,使入侵检测系统实现更高的可用性和稳定性。本文提出了一个基于数据挖掘技术的入侵检测系统框架。     

基于粗糙集和遗传算法的数据挖掘方法

运用粗糙集和遗传算法的理论,为大型的数据挖掘提供了一种新的方法。首先通过粗糙集理论对数据进行预处理,然后对属性简约,最后通过遗传算法进行规则提取,寻找最优解。     

网络信息挖掘在电子商务系统中的应用

描述了网络信息挖掘的含义以及与数据挖掘之间的区别,提出了在电子商务应用中采用网络信息挖掘帮助企业实现最大化利润的方法,最后指出了网络信息挖掘未来的研究方向。     

空间数据挖掘知识的地图可视化表达*

通过分析空间数据挖掘所能发掘的知识类型,对发掘出的知识表达方式进行了研究,提出地图是空间数据挖掘规则和知识的可视化表达的优秀和成熟的表示方法,并对如何表达进行了探讨。     

基于网格技术的全国社会保险网络系统联网的研究

社会保险业务涉及劳动保障、民政、财政、税务、金融、公安等多个部门,需要实现跨地区、跨部门之间的高效、协同工作;网格(Grid) 作为建立在互联网之上的新一代基础设施,近年来在国内外引起了广泛关注。通过网格技术整合社保信息的各种资源,研究全国社会保险网络系统联网的实现。     

Stakeholder orientated sustainable land management: The Ruhr Area as a role model for urban areas

The concept of sustainable land management (SLM) can help create a balance between the different demands of economy, society and nature. SLM means to actively develop land based on a knowledge-based procedure. It should aim on long term enhancements. The basis of SLM is the interests and the goals of the stakeholders. It is the result of a bargaining process between different institutions by creating win-win-situations or adequate conflict solutions to reach a consensus. The Ruhr Area is Germany’s biggest metropolitan area. Land management regarding economic, social and environmental issues, which has a long tradition in this area, goes back to 1920. Being the centre of the heavy industry in Germany for more than 150 years, the Ruhr Area today is a green metropolis. The ecological, social and economical change of this industrial region can serve as a role model for SLM in urban areas all over the world.     

A data mining technique for discovering distinct patterns of hand signs: implications in user training and computer interface design

Hand signs are considered as one of the important ways to enter information into computers for certain tasks. Computers receive sensor data of hand signs for recognition. When using hand signs as computer inputs, we need to (1) train computer users in the sign language so that their hand signs can be easily recognized by computers, and (2) design the computer interface to avoid the use of confusing signs for improving user input performance and user satisfaction. For user training and computer interface design, it is important to have a knowledge of which signs can be easily recognized by computers and which signs are not distinguishable by computers. This paper presents a data mining technique to discover distinct patterns of hand signs from sensor data. Based on these patterns, we derive a group of indistinguishable signs by computers. Such information can in turn assist in user training and computer interface design.