  Toward a computer-assisted marking for descriptive math questions,this paper
presents clustering of online handwritten mathematical expressions (OnHMEs) to
help human markers to mark them efficiently and reliably. We propose a
generative sequence similarity function for computing a similarity score of two
OnHMEs based on a sequence-to-sequence OnHME recognizer. Each OnHME is
represented by a similarity-based representation (SbR) vector. The SbR matrix
is inputted to the k-means algorithm for clustering OnHMEs. Experiments are
conducted on an answer dataset (Dset_Mix) of 200 OnHMEs mixed of real patterns
and synthesized patterns for each of 10 questions and a real online handwritten
mathematical answer dataset of 122 student answers at most for each of 15
questions (NIER_CBT). The best clustering results achieved around 0.916 and
0.915 for purity, and around 0.556 and 0.702 for the marking cost on Dset_Mix
and NIER_CBT, respectively. Our method currently outperforms the previous
methods for clustering HMEs.
