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

这个小型企业创新研究(SBIR)第一阶段项目将在卡内基梅隆大学计算机科学系衍生出NSF赞助的关于知识发现的基础研究。其结果将是商业软件，只需最少的点击和滚动，就可以在一个计算机屏幕上传达数百或数千份文件的内容。这种能力将增强各种信息需求，如搜索、浏览和浏览，并缓解信息过载的问题，这是目前所有基于计算机的文本信息检索者面临的问题。基本方法是一种新形式的概念聚类，它强调结果文档聚类的人类可描述性。这些技术将经典的层次聚类与PI在数据驱动的知识发现方面的研究结果结合在一起，该研究侧重于为大量类(这里是文档聚类)生成非常简洁和对比的描述。总体目标是取代冗长的匹配文档列表显示，这种显示几乎是通用的，但会迫使用户重复且低效地点击、回溯和滚动。潜在的市场机会包括任何返回数十个以上相关匹配以进行典型信息查询的领域，例如网络搜索、新闻、专利、科研摘要、专有公司信息，以及通常由众多专业信息服务提供商提供的内容。

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.