Binary similarity indices are numerical analysis methods used to compare data involving two binary vectors (lists). The scope of this project involved comparing 54 binary similarity indices methods in relationship to binary vector density using the R programming language. Matrices were created of various vector data. The matrices were then scrambled to represent random data. Finally, the data was analyzed and plotted. Vector density variation can result in large differences - in both rate of change relative to density and magnitude. Awareness of these differences is important when selecting an analysis method and understanding the effects of changing vector density on analysis of results.
|Published - 22 Aug 2020
|Oklahoma State University Center for Health Sciences Research Day 2019 - Oklahoma State University Center for Health Sciences, TULSA, United States
Duration: 21 Feb 2019 → 22 Feb 2019
|Oklahoma State University Center for Health Sciences Research Day 2019
|Research Day 2019
|21/02/19 → 22/02/19