Document Type

Poster

Organization

Southwestern Oklahoma State University

Conference Title

26th SWOSU Research & Scholarly Activity Fair

City and State

Weatherford, OK

Conference Date

Novemeber 15, 2018

Publication Date

11-15-2018

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.

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