US-France (INRIA) Cooperative Research: Robust Semi-Supervised Clustering with Application to Multi-Modal Database Categorization

0405166 FriguiThis美国-法国孟菲斯大学的Hichem Frigui研究小组与位于罗克昂古的法国国家信息学和应用数学研究所（INRIA）的Nozha Boujemaa领导的IMEDIA研究小组之间的合作研究项目侧重于开发适用于对大量多模态科学数据集进行分类的有效聚类算法。 该提案涉及聚类算法的理论方面及其在分析和组织科学数据集方面的应用，即：（1）过去50年中产生的植物生物多样性数据中的科学文本和相关图像;（2）来自拟南芥基因组计划的基因表达数据。该项目将利用模糊集理论和统计估计的整合来提高数据聚类方法的知识。 他们将开发新的算法，以识别子空间中的数据集群，联合收割机多模态功能，并使用少量的标记样本来指导聚类过程。更广泛的影响：新的算法，沿着科学数据集，将与美国调查员的基于内容的图像检索（CBIR）原型相结合，该原型由他的NSF-CAREER项目资助。 CBIR原型将用于工程和计算机科学专业的新生以及高中生的教育活动。 该方法在信息安全、生物信息学、基于内容的多媒体和其他大型数据集等领域具有重要的应用价值。通过这个奖项，美国学生有机会在国际研究环境中发展研究技能，并与法国研究人员建立合作伙伴关系，以促进未来的合作。

0405166FriguiThis U.S.-France cooperative research project between Hichem Frigui's research group at the University of Memphis and the IMEDIA research team led by Nozha Boujemaa at the French National Institute for Research in Informatics and Applied Mathematics (INRIA) in Rocquencourt focuses on the development of effective clustering algorithms suitable for categorizing massive multi-modal scientific data collections. The proposal addresses theoretical aspects of clustering algorithms and their applications in analyzing and organizing scientific data sets, namely: (1) scientific text and related images in botanical biodiversity date produced in the last 50 years; and (2) gene expression data from Arabidopsis thaliana genome project.Intellectual Merit: The project will advance knowledge in data clustering methods using integration of fuzzy set theory and statistical estimators. They will develop new algorithms that identify clusters of data in subspaces, that combine multi-modal features, and that use few labeled samples to guide the clustering process.Broader Impacts: The new algorithms, along with the scientific data sets, will be combined with the U.S. investigator's content-based image retrieval (CBIR) prototype funded under his NSF-CAREER project. The CBIR prototype will be used in education activities for freshman engineering and computer science students as well as high school students. The methods developed will be useful for applications in information security, bioinformatics, content-based multimedia and other large data sets. Through this award, U.S. students are given the opportunity to develop research skills in an international research environment and initiate partnerships with French researchers for future collaboration.