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

基本信息

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

项目摘要

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使用。由于计算效率和数据隐私的要求，将来自多个分布式站点的所有数据收集到一个地方进行模式分析（称为模糊聚类）具有挑战性。该项目旨在建立一个新的框架，可以对分布式大数据进行有效的模式分析。可以从几个指标（例如准确性和计算效率）方面深入了解模式分析方法的性能。这项研究的成果将在分布式大数据环境中模式分析的理论和算法的发现和发展方面取得重大进展。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、学生指导和社区外展来招募和教育下一代机器学习科学家和工程师。该项目为大数据应用的模糊聚类奠定了坚实的理论基础。尽管不需要事先假设统计分布，但当数据分布、非正态和高维、混合有分类变量和连续变量以及缺失值时，现有的模糊聚类方法缺乏计算效率。在该项目中，开发了新方法来提高分布式大数据模糊聚类的效率。该项目的工作包括：（1）为分布式大数据开发计算高效的模糊聚类； (2)设计分布式大数据智能模糊聚类框架； (3)通过模拟和实际数据进行性能验证。该项目产生算法、理论模型和实际实施指南，以实现模糊聚类并为科学研究和工程界开发组件。该奖项反映了 NSF 的法定使命，并通过利用基金会的智力优势和更广泛的评估进行评估，认为值得支持。影响审查标准。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Federated Fuzzy Clustering for Longitudinal Health Data

纵向健康数据的联合模糊聚类

DOI：
发表时间：
2022-12
期刊：
IEEEACM Conference on Connected Health Applications Systems and Engineering Technologies CHASE
影响因子：
0
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