数据挖掘可视化技术应用与研究

本文讨论了数据挖掘和可视化的关键技术,提出了运用在油田数据库中的一种可视化模型的设计方法。取出油田数据库的一个数据截面进行可视化,可以直观清晰的看到数据库中的频繁与例外异常模式,对提高决策的效率具有重大现实意义。     

可视化数据挖掘技术研究

总结了目前数据挖掘领域中可视化方法的研究状况。结合国外先进的数据挖掘工具,分析了当前可视化数据挖掘技术的应用现状。基于可视化数据挖掘的任务和目标,阐述了可视化数据挖掘技术的发展趋势。     

可视化数据挖掘方法与技术

可视化数据挖掘技术是可视化技术和数据挖掘技术的有机结合,是数据挖掘技术发展的必然,它涉及到计算机图形学、图像处理、计算机辅助设计、计算机视觉及人机交互技术等多个领域：数据挖掘的可视化已由单纯的模型可视化发展到数据可视化与数据挖掘过程和结果的可视化;本文着重讨论了可视化数据挖掘的分类和相关技术,最后提出了未来的研究方向。     

数据挖掘在电子商务中的应用研究

本文主要介绍数据挖掘在电子商务中的应用,文章从数据挖掘的定义、数据挖掘的常用方法出发,阐述了数据挖掘在海量信息中提取有效信息的作用,特别是数据挖掘与电子商务的结合可以给人们生活带来便利,给企业带来新的利润空间以及为企业提供决策支持。常用的数据挖掘技术主要包括支持向量机、朴素贝叶斯、聚类、决策树、神经网络和关联规则等,最后介绍了数据挖掘在电子商务的4个主要应用方面,说明了数据挖掘存在的必要性。     

数据挖掘技术在电子商务中应用研究

电子商务的蓬勃发展,使得企业之间的合作更为快捷和协调,极大的缩短生产周期,降低了生产成本。利用数据仓库和数据挖掘等现代信息技术,能够构建一个有效的决策支持系统,充分发挥企业独有的优势,促进管理创新和技术创新。     

数据挖掘技术及在电子商务中的应用

数据挖掘（Data Mining)被认为是解决“数据爆炸”和“数据丰富,信息贫乏（Data Rich and information Poor)”是一种有效方法,本文对数据挖掘的基本原理作了阐述,分析了数据挖掘的流程及主要功能,介绍了数据挖掘工具的算法和特点,并结合具体实例说明了数据挖掘在电子商务中的应用。     

电子商务中的数据挖掘系统

本文从概念、基础、体系结构和实际运用等多方面综述了数据挖掘技术的发展及其对电子商务的影响，讨论了我国数据挖掘在WEB中的应用和发展方向。     

数据挖掘在电子商务中的应用

数据挖掘技术作为解决“数据爆炸”时代出现的“信息缺乏”的最有效手段之一,受到了企业界的极大关注。文章阐述了电子商务中数据挖掘技术的框架、数据资源、专业人员、基本方法,分析了数据挖掘在电子商务中的具体应用。     

数据挖掘在电子商务中的应用

本文介绍了数据挖掘技术在电子商务中的应用,给出了数据挖掘过程中采用的一些主要方法,加深了对客户访问行为的了解,有利于更好地调整站点的结构。     

可视化数据挖掘技术及其应用

大型数据库信息管理和信息挖掘技术是国际热门问题之一。利用Ｍａｔｃｏｍ技术将软件Ｍａｔｌａｂ的矩阵计算功能内嵌到ＶＢ环境中,用友好的人机交互界面实现数据库操作与数据分析一体化,形成相关软件,进一步以高阶相关系数能刻画变量相关的特性为基础,乘法地实现数据挖掘的高阶关联分析所需要的矩阵运算和动态作图功能。     

基于大数据时代的数据可视化应用分析

如今随着数据采集、存储和数据分析技术飞速发展,大幅度降低了数据储存和处理的成本,我们即将步入一个大数据时代。大数据时代的改革将海量数据处理变为可能,而且大幅降低了处理成本,促使越来越多跨专业学科的人才投入到大数据的开发应用中来。如何才能让大型数据集变得简单和易于理解,可视化无疑是最有效的途径。对大数据背景下的数据可视化应用展开研究,将有助于我们发展和创新数据可视化技术。     

电子商务中顾客的偏好分析

电子商务中顾客会产生大量的数据信息,利用数据挖掘技术可以从中发现顾客的偏好,以便更好地进行个性化的服务。其中关键之一是要能够正确客观地描述顾客的偏好,本文将重点对此进行分析。     

Research on E-Commerce Transaction Payment System Basedf on C4.5 Decision Tree Data Mining Algorithm

In this paper, according to the information classification algorithm in data mining, data in the network payment system of e-commerce is mined, forming an effective evaluation of the security of the network payment system. Firstly, the method of network security risk prediction is discussed. Secondly, according to the characteristics of network payment system, the system security index system is analyzed in detail, and the specific application process of the C4.5 Classification Algorithm in security evaluation is discussed. Finally, the data mining process is designed in detail and the corresponding code established. In this paper, data mining theory is applied to the network payment security evaluation system, and an algorithm system is constructed to evaluate the network payment security. The algorithm system realizes the effective evaluation and judgment of the network payment system security as well as warning of potential network security problems, effectively changing the previous way of network security management, and ensures the security and stability of the network payment system is maximized.     

An interactive data visualization system

GRAFANA is an interactive program, which allows the user to produce drawings based on data stored on a disk file. The major advantage and originality of the system is that the user can interactively determine the visual effect of a drawing and immediately see it on the screen. Moreover, any drawing produced by GRAFANA can still be edited by a generalpurpose graphics editor. This system allows the user to obtain precisely the right drawing for any book, paper or report. GRAFANA has been implemented in MIRA-2D, a graphical Pascal extension. It is available for different machines and kinds of graphical devices.     

基于Web的数据挖掘研究综述

基于Web数据挖掘是一个结合了数据挖掘和WWW的热门研究主题。文章介绍了Web数据挖掘最流行的分类；Web内容挖掘，Web结构挖掘和Web使用记录挖掘，根据Web数据挖掘的最近研究状况，总结了几个研究热点，并介绍了一个Web使用记录挖掘的框架WebSIFT.     

Design of an integrated data retrieval, analysis, and visualization system: Application in the hydrology domain

In this paper, we present a prototype system, an integrated data management system, which is capable of querying, retrieving, and visualizing datasets with heterogeneous formats and large sizes without requiring users to have any knowledge of any other specific software. Our system has three distinguished characteristics: (1) modular structure and simple architecture which make it easy and feasible for users to add new functions and features to the system, (2) a new search concept and method based on the bounding box and on dynamically delineated watershed boundary from GIS (Geographic Information System), and (3) no requirement on having any knowledge about or installation of any other complicated software. The architecture of our integrated data management system is based on a metadata approach, which consists of four components including a metadata mechanism and a Java-based application engine. The metadata mechanism in conjunction with the Java-based application engine allows users to access and retrieve diverse data formats and structures from many heterogeneous hydrological data sources. The visualization component of the system makes it possible for users to view their queried data first before spending time retrieving them. The extensible and integrative characteristics of our system are illustrated by an example in which new and unique functions for data merging and GIS-based data querying are added to the system. Although the data sources and applications shown in this prototype system are related to the field of hydrology, the ideas, approaches, and system architecture are not domain-specific, and can be used/applied to other fields as well.     

数据挖掘技术在CRM中的应用研究

本文介绍了CRM以及数据挖掘的基本概念,讲述了数据挖掘的常用算法以及客户关系管理中数据挖掘的流程,并用实例讲解了数据挖掘算法在CRM中的应用。     

Future trends in data mining

Over recent years data mining has been establishing itself as one of the major disciplines in computer science with growing industrial impact. Undoubtedly, research in data mining will continue and even increase over coming decades. In this article, we sketch our vision of the future of data mining. Starting from the classic definition of “data mining”, we elaborate on topics that — in our opinion — will set trends in data mining.