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Clayton Miller added UWC_kmeans_clustering1.tex
almost 10 years ago
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The next step in the process is to use k-means clustering to further aggregate the motif candidates. We chose in this case to group the day-types into five clusters based on the six motif candidates and the similarity between the $aaaa$ and $aaba$ patterns. The results of this clustering are seen in Figure~\ref{fig:clusters1}. Clusters 0 is strongly prevalent on the weekends, while clusters 2, 3, and 4 are weekday dominant. Cluster 1 correlates with days in which the facility is partially open due to a teacher working day without students or extracurricular activities.
