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

聚类分析是数据挖掘者武器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理解数据的结构。近年来，双模态和多模态数据的出现越来越多，它们表现为二维矩阵和高维张量。对于此类数据的探索性分析，共聚类正在成为一种日益流行的技术，并已成功地应用于广泛的领域，包括网络挖掘、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。该项目的总体目标是发展各种类型的共聚类分析的合理的理论公式，使共聚类分析成为双模态和多模态数据探索性分析中不可或缺的有效工具。本研究的重点是扩展共聚类分析，以包括多维张量，其中人们希望同时在两个以上的模式上聚类，以及具有添加行和列属性的矩阵数据，例如描述网络知识结构或多个相互关联的表的矩阵数据。这将使协同集群能够覆盖更广泛的应用程序类别，并使其在计算上具有实用性。为了扩大该项目的影响，主要研究人员正在联合组织研讨会，以促进和促进对共同聚类分析的各个方面的研究。在这个项目下开发的数据、论文和软件将通过项目网站（http://hercules.ece.utexas.edu/~ghosh/scalclust.html）与科学界共享。最后，作为社区外展的一部分，研究人员计划设计外展模块，以适合高中生和新生的水平说明数据分析概念和能力。

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.