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Target Sequence Clustering

Clustering algorithms are powerful methods for understanding the structure of data. However, in many applied domains, researchers are only interested in clustering algorithms if they can produce clusters that explain external variables. For example, in education research, clusters of student behaviors are only useful if they help predict student learning. Generally, clustering algorithms are not explicitly designed to handle this additional constraint. I propose that incorporating these external variables directly into clustering algorithms can improve both cluster relevancy and model generalization. This approach, called target sequence clustering, has shown promising results on multiple data sets. While I will demonstrate the effectiveness of this approach using educational data sets, I will also illustrate how the method can be applied more broadly.