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Hierarchical Probabilistic Segmentation of Discrete Events
2009
2009 Ninth IEEE International Conference on Data Mining
Segmentation, the task of splitting a long sequence of discrete symbols into chunks, can provide important information about the nature of the sequence that is understandable to humans. Algorithms for segmenting mostly belong to the supervised learning family, where a labeled corpus is available to the algorithm in the learning phase. We are interested, however, in the unsupervised scenario, where the algorithm never sees examples of successful segmentation, but still needs to discover
doi:10.1109/icdm.2009.87
dblp:conf/icdm/ShaniMG09
fatcat:mxdhuemvd5effnraav3o4nap7e