III-COR: Versatile Co-clustering Analysis for Bi-modal and Multi-modal Data

III-COR：双模态和多模态数据的多功能共聚类分析

• 批准号: 0713142
• 负责人: Joydeep Ghosh
• 金额: $ 43万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713142&HistoricalAwards=false
• 关键词: III; COR; Versatile; Co; clustering

Cluster analysis is an indispensable tool in the data miner's arsenal which enables one to understand the structure of the data while conducting exploratory data analysis. Recent times have seen increased occurrences of bi-modal and multi-modal data that manifest themselves as two-dimensional matrices and higher-dimensional tensors. Co-clustering is becoming an increasingly popular technique for exploratory analysis of such data, and has been successfully applied in wide range of areas, including web mining, natural language processing, image and video content analysis, recommender systems, and bioinformatics . The broad goal of this project is to develop sound, theoretical formulations of varied types of co-cluster analyses so that co-cluster analysis becomes an indispensable and efficient tool in the exploratory analysis of bi-modal and multi-modal data. This research focuses on extending co-cluster analysis to include multi-dimensional tensors where one desires to cluster on more than two modes simultaneously, and matrix data with added row and column attributes such as those describing networked knowledge structures or multiple interlinked tables. This will enable co-clustering to reach a much wider class of applications and also make it computationally practical. In order to broaden the impact of this project, the principal investigators are jointly organizing workshops that foster and promote research on various aspects of co-cluster analysis. Data, papers and software developed under this project will be shared with the scientific community via the project Web site (http://hercules.ece.utexas.edu/~ghosh/scalclust.html). Finally, as part of community outreach, the investigators plan to design outreach modules that illustrate data analysis concepts and capabilities at levels appropriate for high school students as well as for freshmen students.

聚类分析是数据挖掘工具库中不可或缺的工具，它使人们能够在进行探索性数据分析时了解数据的结构。最近，出现了越来越多的双峰和多峰数据，表现为二维矩阵和高维张量。协同聚类正在成为一种越来越流行的探索性分析此类数据的技术，并已成功应用于广泛的领域，包括Web挖掘，自然语言处理，图像和视频内容分析，推荐系统和生物信息学。该项目的主要目标是开发各种类型的共聚类分析的合理的理论公式，使共聚类分析成为探索性分析双峰和多峰数据中不可或缺的有效工具。本研究的重点是扩展共聚类分析，包括多维张量，其中一个愿望，以集群在两个以上的模式同时，和矩阵数据添加行和列属性，如那些描述网络知识结构或多个相互关联的表。这将使联合聚类能够达到更广泛的应用类别，并使其在计算上实用。为了扩大这一项目的影响，主要调查人员正在联合举办讲习班，以促进和推动对共聚类分析各个方面的研究。在该项目下开发的数据、论文和软件将通过项目网站（http：//hercules.ece.utexas.edu/taghosh/scalclust.html）与科学界分享。最后，作为社区外展的一部分，研究人员计划设计外展模块，以说明适合高中生和新生的数据分析概念和能力。