EAGER: IIS: Enabling Computationally Efficient Fuzzy Clustering for Distributed Big Data

EAGER：IIS：为分布式大数据启用计算高效的模糊聚类

• 批准号: 2140729
• 负责人: Hua Fang
• 金额: $ 10万
• 依托单位: University of Massachusetts, Dartmouth
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140729&HistoricalAwards=false
• 关键词: EAGER; IIS; Enabling; Computationally; Efficient

Distributed big data are widely available now. Gathering all the data from multiple distributed sites to one place for pattern analysis (called fuzzy clustering) is challenging due to the requirements of computational efficiency and data privacy. This project aims to establish a new framework that can enable efficient pattern analysis for distributed big data. Deep understanding can be gained on the performance of the pattern analysis methods in terms of several metrics, such as accuracy and computational efficiency. The outcomes of this research will constitute a significant advance in the discovery and development of theories and algorithms for pattern analysis in distributed big data environments. This knowledge can be used for wide range of applications, such as healthcare and transportation. The research project is tightly coupled to a vital education component. The project recruits and educates the future generation of machine learning scientists and engineers through curriculum development, student mentoring, and community outreach.This project develops a strong theoretical underpinning for fuzzy clustering for Big Data applications. Existing fuzzy clustering approaches lack computational efficiency when the data are distributed, non-normal and high-dimensional, with a mix of categorical and continuous variables and missing values, although no prior assumptions of statistical distributions are required. In this project, new approaches are developed to augment the efficiency of fuzzy clustering for distributed Big Data. The works in the project include: (1) developing computational efficient fuzzy clustering for distributed Big Data; (2) designing a framework for intelligent fuzzy clustering over distributed Big Data; (3) performance validations through both simulations and real data. This project produces algorithms, theoretical models, and guidelines for practical implementation to enable fuzzy clustering and develop components for the scientific research and engineering communities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

分布式大数据现在已广泛可用。由于计算效率和数据隐私的要求，将所有数据从多个分布式站点收集到一个地方进行模式分析（称为模糊聚类）是具有挑战性的。该项目旨在建立一个新框架，该框架可以为分布式大数据启用有效的模式分析。可以从几种指标（例如准确性和计算效率）方面获得有关模式分析方法的性能的深刻理解。这项研究的结果将在分布式大数据环境中的模式分析的理论和算法的发现和开发中构成重大进步。这些知识可用于广泛的应用，例如医疗保健和运输。该研究项目与重要的教育组成部分紧密结合。该项目通过课程开发，学生指导和社区宣传来招募和教育机器学习科学家和工程师的未来一代。该项目为大数据应用程序开发了强大的理论基础，以进行大数据应用程序。当数据分布时，现有的模糊聚类方法缺乏计算效率，非正态和高维度，尽管不需要对统计分布的先前假设，但具有分类和连续变量和缺失值的混合。在该项目中，开发了新方法来提高分布式大数据的模糊聚类效率。项目中的作品包括：（1）为分布式大数据开发计算有效的模糊聚类； （2）设计一个智能模糊聚类的框架，而不是分布式大数据； （3）通过模拟和真实数据验证性能验证。该项目生成了实施算法，理论模型和指南，以使科学研究与工程社区的模糊聚类和开发组成部分。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的。

项目成果

Federated Fuzzy Clustering for Longitudinal Health Data

纵向健康数据的联合模糊聚类

• 发表时间: 2022
• 期刊: IEEEACM Conference on Connected Health Applications Systems and Engineering Technologies CHASE
• 作者: Balkus, Salvador;Fang, Hua;Wang, Honggang
• 通讯作者: Wang, Honggang

Intelligent fuzzifier-based cluster validation for incomplete longitudinal digital trial data

基于智能模糊器的不完整纵向数字试验数据聚类验证

• 发表时间: 2022
• 期刊: IEEEACM Conference on Connected Health Applications Systems and Engineering Technologies CHASE
• 作者: Ngo, Hieu;Fang, Hua;Wang, Honggang
• 通讯作者: Wang, Honggang