Unsupervised Document Set Exploration Using Divisive Partitioning

使用分裂分区进行无监督文档集探索

• 批准号: 9811229
• 负责人: Daniel Boley
• 金额: $ 18万
• 依托单位: University of Minnesota-Twin Cities
• 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: 1998-09-15 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9811229&HistoricalAwards=false
• 关键词: Unsupervised; Document; Set; Exploration; Using

The purpose of this project is to develop algorithms and tools for the exploration and categorization of extremely large bodies of documents, especially from the World Wide Web. The technical approach is based on a new hierarchical divisive partitioning method which has produced quality clusters very fast in preliminary tests. The research issues to be addressed include: scalability analysis, theoretical foundations, incremental updating methods, generalizations (such as handling missing values and different scaling), and interface to one or more Web agents for various applications. Educational seminars and tutorials are a natural part of this project, given its interdisciplinary nature. Anticipated results are a set of algorithms and tools for organizing large document collections that enjoy the features of (1) scalability to very large datasets, (2) unsupervised operation, and (3) reasonable quality and usefulness of the categories found. Anticipated benefits include an order of magnitude increase in the size of datasets on which it will be practical to extract useful categories in an unsupervised manner. Potential applications include client-side WWW organization and search aids, server-side aids to create document ratings in a consistent manner, tools to maintain and update organization and classification of contents of specialized databases, all with a minimum of human intervention. http://www.cs.umn.edu/~boley/PDDP.html

这个项目的目的是开发算法和工具，用于探索和分类极大的文档，特别是来自万维网的文档。该技术方法基于一种新的等级划分划分方法，该方法在初步测试中非常快速地产生了质量集群。要解决的研究问题包括：可伸缩性分析、理论基础、增量更新方法、泛化(如处理缺失值和不同的伸缩性)以及与不同应用的一个或多个Web代理的接口。鉴于该项目的跨学科性质，教育研讨会和教程是该项目的自然组成部分。预期的结果是一套用于组织大型文档集合的算法和工具，这些算法和工具具有以下特征：(1)可扩展到非常大的数据集，(2)无监督操作，以及(3)所发现类别的合理质量和有用性。预期的好处包括数据集的大小增加了一个数量级，在这些数据集上以无监督的方式提取有用的类别将是切实可行的。潜在的应用包括客户端WWW组织和搜索辅助工具、以一致方式创建文档评级的服务器端辅助工具、维护和更新专门数据库内容的组织和分类的工具，所有这些都只需最少的人工干预。Http://www.cs.umn.edu/~boley/PDDP.html