CAREER: Scalable Algorithms for Knowledge Discovery in Scientific Data Sets

数据挖掘是自动提取隐藏在大型数据集中的有用信息的过程。随着数据收集的进步导致可用数据量的爆炸性增长，这一新兴学科正变得越来越重要。该项目旨在开发广泛的新型数据挖掘算法，适用于基因组学和流体动力学中出现的科学数据集的特征。我们的研究将集中于为顺序数据集和可以用有向标记图表示的数据集开发算法。基于图形的建模使我们能够在一个单一和统一的框架中捕获科学数据集中存在的许多空间、拓扑、几何和其他类型的关系特征。我们计划解决的具体研究任务是：(i)开发用于查找图数据集中频繁出现的模式的可扩展算法，以及用于查找频率随模式长度而减小的模式的算法。（ii）为直接在本地特征空间中操作的序列和图形数据集开发可扩展和高质量的聚类算法。（iii）基于自动顺序或关系特征提取方法开发可扩展和准确的分类算法。这些算法将通过分析基因组和湍流流体流动中产生的数据集来验证。该项目将数据挖掘研究与一个教育计划相结合，该计划侧重于启动本科生和研究生基因组研究的各种计算和数据分析方面，并开发一个全面的生物信息学课程，其目标是促进多学科研究和合作。此外，将开发和提供一套全面的软件工具，既可以用来训练学生使用数据挖掘技术，也可以用来进行新的研究，扩大对各种科学学科的理解水平。

Data mining is the process of automatically extracting useful information hidden in large data sets. This emerging discipline is becoming increasingly important as advances in data collection have lead to the explosive growth in the amount of available data. This project aims to develop a wide-range of novel data mining algorithms suitable for the characteristics of scientific data sets arising in genomics and fluid dynamics. Our research will focus on developing algorithms both for sequential datasets and for datasets that can be represented by directed labeled graphs. The graph-based modeling enables us to capture in a single and unified framework many of the spatial, topological, geometric, and other types of relational characteristics present in scientific datasets. The specific research tasks that we plan to address are: (i) Development of scalable algorithms for finding frequently occurring patterns in graph data sets and algorithms for finding patterns whose frequency decreases as a function of the pattern-length. (ii) Development of scalable and high quality clustering algorithms for sequence and graph data sets which operate directly in the native feature space. (iii) Development of scalable and accurate classification algorithms based on automated sequential or relational feature extraction approaches. These algorithms will be validated by analyzing data sets arisingin genomic and turbulent fluid flow. The project integrates the data mining research with an educational plan that focuses on initiating undergraduate and graduate students to the various computational and data analysis aspects of genomic research and developing a comprehensive bioinformatics curriculum whose goal is to foster multi-disciplinary research and collaboration. In addition, a comprehensive set of software tools will be developed and made available that can be used both to train students in using data mining techniques and to conduct novel research expanding the levels of understanding in various scientific disciplines.