| dc.description.abstract | Clustering methods optimize the partitioning of data points with respect to an internal metric, such as likelihood, in order to approximate the goodness of clustering. However, this internal metric does not necessarily translate into effective clustering from the user's perspective. This work presents the interactive Bayesian Case Model (iBCM), a model that opens a communication channel between the clustering model and the user. Users can provide direct input to iBCM in order to achieve effective clustering results, and iBCM optimizes the clustering by creating a balance between what the data indicate and what makes the most sense to the user. This model provides feedback for users and does not assume any prior knowledge of machine learning on their part. We provide quantitative evidence that users are able to obtain more satisfactory clustering results through iBCM than without an interactive model. We also demonstrate the use of this method in a real-world setting where computer language class teachers utilize iBCM to cluster students' coding assignments for grading. | en_US |
