Work

kluster: An Efficient Scalable Procedure for Approximating the Number of Clusters in Unsupervised Learning

Year: 2018

Type: article

Source: Big Data Research

DOI: 10.1016/j.bdr.2018.05.003

Authors: Hossein Estiri, Behzad Abounia Omran, Shawn N. Murphy

Institutions: Harvard University, Massachusetts General Hospital, The Ohio State University

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: closed

Funders: Patient-Centered Outcomes Research Institute, National Institutes of Health, U.S. National Library of Medicine

Awards: G1393766945, G560201083, G9000036806

OpenAlex ID: W2806794445

FWCI: 1.131

Cites: 47

Cited by: 18

Related to: 10