III-COR-Small: Relational Data Community Discovery and Learning

该项目的目标是对一系列基本的，开放的，但非常重要的问题进行深入研究，从而形成一个统一的理论，包括一般关系数据社区发现和学习的革命性方法。具体的方法包括统计机器学习和数据挖掘的新方法，统一的观点链接到机器学习和数据挖掘领域的现有文献。知识价值包括对一般无监督关系数据聚类和学习的全面理解，以及社区发现和学习方法的预期突破。更广泛的影响包括促进及时和有效的知识传播关系数据挖掘和统计机器学习，包括项目？网站（http：//www.fortune.binghamton.edu/nsf-iis-0812114.htm），以及各种应用的技术开发和转让。教育和外联活动包括为大学生提供教育和研究经验。此外，活动强调推进和加强高中教育和科学教学大纲，并为高中研究和为当地社区及其他地区服务制定综合模式。

The goal of this project is to conduct an in-depth research on a series of fundamental, open, but very important issues leading to the development of a unified theory consisting of revolutionary methods on the general relational data community discovery and learning. The specific approaches include novel methods on statistical machine learning and data mining with unified views to link to the existing literature in the areas of machine learning and data mining. The intellectual merit includes the development of comprehensive understanding of the general unsupervised relational data clustering and learning as well as the expected breakthrough in the community discovery and learning methodologies. The broader impacts include promoting the timely and effective knowledge dissemination on relational data mining and statistical machine learning, including project?s web site (http://www.fortune.binghamton.edu/nsf-iis-0812114.htm), as well as the technology development and transfer in a wide range of applications. Educational and outreach activities include providing educational and research experience for the university students. In addition, activities emphasize advancing and enhancing the high school education and syllabi in sciences and developing an integrated model for high schools' research and services for the local community and beyond.