SBIR Phase I: Concise Visualization of a Document Collection via Conceptual Clustering

这个小企业创新研究（SBIR）第一阶段项目将在卡内基梅隆大学计算机科学系开展NSF赞助的知识发现基础研究。 其结果将是商业软件，它可以在一个计算机屏幕上传送数百或数千个文件的内容，只需最少的点击和滚动。 这一能力将增强搜索、浏览和浏览等多种多样的信息需求，并缓解信息过载的问题，这是当今所有基于计算机的文本信息检索者所面临的问题。 基本的方法是一种新形式的概念聚类，强调人类的可描述性所产生的文档集群。 该技术结合了联合收割机经典的层次聚类与PI的研究结果的数据驱动的知识发现，集中在生成非常简洁和对比的描述大量的类（这里，文档集群）。 总体目标是取代冗长的匹配文档排序列表显示，这几乎是普遍的，但这迫使用户重复和低效的点击，回溯和滚动。潜在的市场机会包括任何领域，其中超过几十个相关匹配返回典型的信息查询，如网络搜索，新闻，专利，科学研究摘要，专有企业信息，以及通常由专业信息服务的众多供应商提供的内容。

This Small Business Innovation Research (SBIR) Phase I project will spin off NSF-sponsored basic research on knowledge discovery at Carnegie Mellon University computer science department. The result will be commercial software that can convey the contents of hundreds or thousands of documents on one computer screen with minimal clicking and scrolling. This capability will enhance information needs as diverse as search, overviewing, and browsing, and alleviate the problem of information overload, which today confronts all retrievers of computer-based textual information. The basic approach is a new form of conceptual clustering that emphasizes the human describability of the resulting document clusters. The techniques combine classical hierarchical clustering with results from the PI's research on data-driven knowledge discovery, which focused on generating very concise and contrastive descriptions of a large number of classes (here, document clusters). The overall goal is to replace the tedious long ranked list display of matching documents, which is nearly universal, but which forces users into repeated and inefficient clicking, backtracking, and scrolling.The potential market opportunities include any domain where more than a few dozen relevant matches are returned for typical information queries, such as web searches, news, patents, scientific research abstracts, proprietary corporate information, and, generally the content delivered by the numerous vendors of specialized information services.