Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn ...
Lei Li, James McCann, Nancy S. Pollard, Christos F...
Biosequences typically have a small alphabet, a long length, and patterns containing gaps (i.e., “don’t care”) of arbitrary size. Mining frequent patterns in such sequences ...
Background: Traditional algorithms for hidden Markov model decoding seek to maximize either the probability of a state path or the number of positions of a sequence assigned to th...
In this paper, we present a new hypergraph partitioning algorithm that is based on the multilevel paradigm. In the multilevel paradigm, a sequence of successively coarser hypergra...
George Karypis, Rajat Aggarwal, Vipin Kumar, Shash...
Data mining is widely used to identify interesting, potentially useful and understandable patterns from a large data repository. With many organizations focusing on webbased on-lin...
Abhinav Srivastava, Shamik Sural, Arun K. Majumdar