CAREER: Probabilistic Knowledge Discovery and Data Mining: An Integrated Approach at the Interface of ComputerScience and Statistics

这个项目涉及到计算机科学、数学和统计学的思想的整合， 它们应用于知识发现和非常大的数据集的数据挖掘。项目 有两个研究目标。第一个涉及开发新的勘探方法， 多元数据结构识别，特别强调聚类和密度 估计。第二个研究目标是开发新的方法来模拟连续的 数据结构，特别是使用图形模型来促进模型构建过程 和估计。技术方法是基于现代统计建模的思想耦合 with computational计算techniques技术.这项工作的一个主要特点是使用大规模的科学和 工程数据集作为试验台，包括高层大气时空数据记录， 医学数据集，包括用于阿尔茨海默病研究的异构数据类型，行星 地质特征的成像数据集和相关注释和目录，以及多变量工程 来自在线系统监控的传感器数据。该项目的教育部分包括： 开发新的课程，强调对模型探索的第一原理理解， 数据分析的背景，以及学生参与跨学科，大规模探索性数据挖掘项目的机会。该项目可以对如何大数据集产生重大影响 在各种科学，医学和商业学科中进行探索和分析。

This project involves the integration of ideas from computer science, mathematics, and statistics, in the context of their application to knowledge discovery and data mining of very large data sets. The project has two general research goals. The first involves the development of novel methods for exploration and identification of structure in multivariate data, with particular emphasis on clustering and density estimation. The second research goal is the development of novel methods for modeling sequential structure in data, in particular the use of graphical models to facilitate the process of model construction and estimation. The technical approach is based on the coupling of ideas from modern statistical modeling with computational techniques. A key feature of this work is the use of large-scale scientific and engineering data sets as testbeds, including upper-atmosphere spatio-temporal data records, a large medical data set consisting of heterogeneous data types for the study of Alzheimer's disease, planetary image data sets and associated annotations and catalogs of geologic features, and multivariate engineering sensor data from online system monitoring. The educational component of the project consists of the development of new courses which emphasize a first-principles understanding of model-exploration in the context of data analysis, as well as opportunities for students to participate in inter-disciplinary, large- scale exploratory data mining projects. This project can have a significant impact on how large data sets are explored and analyzed across a wide variety of scientific, medical, and business disciplines.