CAREER: Scalable Algorithms for Knowledge Discovery in Scientific Data Sets

职业：科学数据集中知识发现的可扩展算法

• 批准号: 0133464
• 负责人: George Karypis
• 金额: $ 32.07万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-02-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0133464&HistoricalAwards=false
• 关键词: CAREER; Scalable; Algorithms; Knowledge; Discovery

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.

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，这一新兴学科变得越来越重要。该项目旨在开发一系列适合基因组学和流体动力学中科学数据集特征的新颖数据挖掘算法。我们的研究将集中于开发用于顺序数据集和可以由有向标记图表示的数据集的算法。基于图的建模使我们能够在一个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）开发可扩展的算法，用于查找图形数据集中频繁出现的模式，以及用于查找频率随模式长度而降低的模式的算法。 (ii) 为直接在本机特征空间中运行的序列和图数据集开发可扩展的高质量聚类算法。 (iii) 基于自动顺序或关系特征提取方法开发可扩展且准确的分类算法。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进行验证。该项目将数据挖掘研究与教育计划相结合，重点是让本科生和研究生了解基因组研究的各个计算和数据分析方面，并开发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，还将开发并提供一套全面的软件工具，可用于培训学生使用数据挖掘技术，并进行新颖的研究，扩大对各个科学学科的理解水平。