High Performance Rough Sets Data Analysis in Data Mining

数据挖掘中的高性能粗糙集数据分析

• 批准号: 0514750
• 负责人: Yi Pan
• 金额: $ 13.75万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0514750&HistoricalAwards=false
• 关键词: Performance; Rough; Sets; Data; Analysis

Data mining (aka Knowledge Discovery in Databases, KDD) is a procedure to extract previously unknown and potentially useful information or pattern from huge data sets. KDD is usually a multiphase process involving numerous steps such as data preparation, data preprocessing, feature selection, rule induction, knowledge evaluation and deployment etc. Many novel data mining and learning algorithms have been developed, though vigorously, under rather add hoc and vague concepts. These algorithms, in most cases, are individual creations of different researchers, without much common methodological and fundamental framework. In other words, great majority of work in data mining is focused on algorithm development while neglecting the studies of fundamental theoretical issues concerning data, inter-data relationships, and quality of the implicit information hidden in the data or data redundancies. Thus, it is not easy to fully understand and evaluate how individual phase influences each other and the impact of each phase on the whole knowledge discovery process. For further development and breakthroughs in data mining and learning algorithms, a deep examination of its foundation is necessary. The central goal of the proposed research is to develop a unified rough set based data mining framework to explore various fundamental issues of data mining and learning algorithms. It aims to present the analytical capabilities of the methodology of rough sets in the context of data mining methodologies, techniques and applications. It will provide a unified framework to help better understand the whole KDD process.Intellectual merit: Rough set theory is particularly suited to reasoning about imprecise or incomplete data and discovering relationships in the data. The simplicity and mathematical clarity of rough set theory makes it attractive for both theoreticians and application-oriented researchers. The main advantage of rough set theory is that it does not require any preliminary or additional information about the data, such as probability in statistics, basic probability assignment in Dempster-Shafer theory or the value of membership in fuzzy set theory. Rough set theory constitutes a sound basis for KDD and can be used in different phases of the KDD process. In particular, the formal techniques of rough set theory lead to many novel and promising breakthrough methods and algorithms for attribute functional, orpartial functional dependencies, their discovery, analysis, and characterization, feature election, feature extraction, data reduction, decision rule generation, and pattern extraction (templates, association rules) etc., which are the fundamental issues of the KDD process. Rough set theory represents a new innovative approach and can lead to the development of new learning algorithms to create novel uses and breakthroughs of data mining techniques.Broader impacts: The proposed collaborative project is interdisciplinary in nature. It will synthesize often-disparate work in data mining, rough set theory and high performance computing. The PIs' strong multidisciplinary research collaboration experience will lead to widespread awareness and impact of the proposed research to rough set, data mining and high performance computing community. It will design and develop a wide-range of novel data mining algorithms and methods including data reduction, rule induction and classification ensemble in one unified framework to better understand the whole KDDprocess. These algorithms and methods will significantly extend the application scope of data mining techniques and rough set theory and will result in the improved understanding of issues involved in designing efficient and innovative data mining and learning algorithms and methods. The proposed research will integrate tightly with teaching activities, the research results will be developed into undergraduate and graduate courses and research projects. Part of this approach includes the development of new cross-disciplinary courses that bring together computer science and mathematics for the understanding of principle and methods of theoretical foundations of data mining and rough set theory. The integration will help with training students in the issues involved in the rough set theory, design and implementation of novel data mining methods and algorithms, high performance computing. The active participation of students will allow for significant exposure to the latest research in datamining.

数据挖掘（又名数据库知识发现，KDD）是从庞大数据集中提取以前未知且可能有用的信息或模式的过程。 KDD通常是一个多阶段的过程，涉及许多步骤，例如数据准备、数据预处理、特征选择、规则归纳、知识评估和部署等。许多新颖的数据挖掘和学习算法已经被开发出来，尽管很活跃，但概念相当附加和模糊。 。这些算法在大多数情况下是不同研究人员的个人创造，没有太多共同的方法论和基本框架。换句话说，数据挖掘的大部分工作都集中在算法开发上，而忽视了对数据、数据间关系、隐藏在数据中的隐含信息的质量或数据冗余等基础理论问题的研究。因此，要完全理解和评估各个阶段如何相互影响以及每个阶段对整个知识发现过程的影响并不容易。数据挖掘和学习算法的进一步发展和突破，需要对其基础进行深入研究。该研究的中心目标是开发一个基于统一粗糙集的数据挖掘框架，以探索数据挖掘和学习算法的各种基本问题。它旨在展示数据挖掘方法、技术和应用背景下粗糙集方法的分析能力。它将提供一个统一的框架，帮助更好地理解整个 KDD 过程。 智力优点：粗糙集理论特别适合推理不精确或不完整的数据并发现数据中的关系。粗糙集理论的简单性和数学清晰度使其对理论家和应用型研究人员都具有吸引力。粗糙集理论的主要优点是它不需要任何关于数据的初步或附加信息，例如统计中的概率、Dempster-Shafer 理论中的基本概率分配或模糊集理论中的隶属度值。粗糙集理论为 KDD 奠定了坚实的基础，可用于 KDD 过程的不同阶段。特别是，粗糙集理论的形式化技术带来了许多新颖且有前途的突破性方法和算法，用于属性泛函、或部分函数依赖、它们的发现、分析和表征、特征选择、特征提取、数据缩减、决策规则生成等。模式提取（模板、关联规则）等，这是KDD过程的基本问题。粗糙集理论代表了一种新的创新方法，可以促进新学习算法的开发，从而创造数据挖掘技术的新用途和突破。更广泛的影响：所提出的合作项目本质上是跨学科的。它将综合数据挖掘、粗糙集理论和高性能计算方面经常不同的工作。 PI 强大的多学科研究合作经验将导致所提议的研究对粗糙集、数据挖掘和高性能计算社区产生广泛的认识和影响。它将在一个统一的框架中设计和开发一系列新颖的数据挖掘算法和方法，包括数据缩减、规则归纳和分类集成，以更好地理解整个 KDD 过程。这些算法和方法将显着扩展数据挖掘技术和粗糙集理论的应用范围，并将提高对设计高效和创新的数据挖掘和学习算法和方法所涉及问题的理解。拟议的研究将与教学活动紧密结合，研究成果将开发成本科生和研究生的课程和研究项目。这种方法的一部分包括开发新的跨学科课程，将计算机科学和数学结合起来，以理解数据挖掘和粗糙集理论的理论基础的原理和方法。该集成将有助于培训学生涉及粗糙集理论、新颖数据挖掘方法和算法的设计和实现、高性能计算等问题。学生的积极参与将使他们能够大量接触数据挖掘的最新研究。