Document Type

Poster

Organization

Southwestern Oklahoma State University

Conference Title

2019 Oklahoma Research Day

City and State

Weatherford, OK

Conference Date

Mar 08, 2019

Publication Date

3-8-2019

Abstract

Machine Learning (ML) is a growing topic within Computer Science with applications in many fields. One open problem in ML is data separation, or data clustering. Our project is a validation study of, “Constrained k-means Clustering with Background Knowledge" by Wagstaff et. al. Our data validates the finding by Wagstaff et. al., which shows that a modified k-means clustering approach can outperform more general unsupervised learning algorithms when some domain information about the problem is available. Our data suggests that k-means clustering augmented with domain information can be a time efficient means for segmenting data sets. Our validation study focused on six classic data sets used by Wagstaff et. al. and does not consider the GPS data of the original study. We have published our code on a public SWOSU Github repository to enable other researchers to use our code as a starting point. Validation studies such as this provide great learning opportunities for students interested in working with Machine Learning, Artificial Intelligence, and other related applications. This research was funded in part by the Dr. Snowden Memorial Scholarship with the NASA OKLAHOMA Space Grant Consortium. This material is based upon work supported by the National Aeronautics and Space Administration issued through the Oklahoma Space Grant Consortium.

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