Web Personalization and Mining Using Robust Fuzzy Clustering Methods

使用鲁棒模糊聚类方法进行 Web 个性化和挖掘

• 批准号: 9800899
• 负责人: Anupam Joshi
• 金额: $ 20.42万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9800899&HistoricalAwards=false
• 关键词: Web; Personalization; Mining; Using; Robust

This is an inter-institutional collaborative project carried out by Anupam Joshi at the University of Maryland, Baltimore County and Raghu Krishnapuram at the Colorado School of Mines. The goal of this research is to develop scalable robust techniques to model noisy data sets containing an unknown number of overlapping categories, and apply them to create a software tool for web personalization and mining. Personalization has two components: (1) tailoring the content delivered to the user from a web site; and (2) exploring the available web pages and categorizing them. The approach consists of developing new practical clustering algorithms by combining fuzzy methods, robust statistics, with Monte Carlo/bootstrapping techniques to model an unknown number of overlapping sets in the presence outliers. Since many web objects such as URLs, IP addresses, and web pages cannot be represented by numerical features, new techniques to handle web objects with linguistic and textual features, as well as to categorize or cluster them by using suitable similarity measures between such objects, are being explored. A software tool for web personalization and mining, which incorporates the algorithms developed into a software architecture, is being created and validated. The results of this project will generate new theoretical results and efficient algorithms for simultaneously estimating the parameters of an unknown number of overlapping categories from noisy data sets, as well as a web personalization and mining tool that will be made available on the web. Thus, this project is expected to have a significant impact on the way documents are searched for and delivered. It will directly influence the usefulness and spread of the Internet and WWW, and in general, will contribute to the digital library technology. http://www.cecs.missouri.edu/~joshi/web-mine/

这是一个机构间合作项目，由马里兰大学巴尔的摩分校的 Anupam Joshi 和科罗拉多矿业学院的 Raghu Krishnapuram 开展。这项研究的目标是开发可扩展的稳健技术来对包含未知数量重叠类别的噪声数据集进行建模，并将其应用于创建用于网络个性化和挖掘的软件工具。个性化有两个组成部分：(1) 定制从网站传递给用户的内容； (2) 探索可用的网页并对它们进行分类。该方法包括通过将模糊方法、鲁棒统计与蒙特卡罗/自举技术相结合来开发新的实用聚类算法，以对存在异常值的未知数量的重叠集进行建模。由于许多 Web 对象（例如 URL、IP 地址和网页）无法用数字特征表示，因此采用新技术来处理具有语言和文本特征的 Web 对象，以及通过在这些对象之间使用适当的相似性度量来对它们进行分类或聚类，正在探索中。正在创建和验证用于网络个性化和挖掘的软件工具，该工具将开发的算法合并到软件架构中。该项目的结果将产生新的理论结果和有效的算法，用于从噪声数据集中同时估计未知数量的重叠类别的参数，以及将在网络上提供的网络个性化和挖掘工具。因此，该项目预计将对文档的搜索和交付方式产生重大影响。它将直接影响互联网和万维网的实用性和传播，并且总体上将为数字图书馆技术做出贡献。 http://www.cecs.missouri.edu/~joshi/web-mine/