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

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.