Cross-partition clustering: revealing corresponding themes across related datasets

This article studies the task of discovering correspondences across related domains based on real-world data collections. We address this task through a designated extension of distributional data-clustering methods. The method is empirically demonstrated on synthetic data as well as on texts addressing different religions, where the goal is to identify commonalities shared by all religions. This article generalises and demonstrates the empirical improvement relative to our previous studies on this subject, as well as to other comparable methods.

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