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Non-redundant Multi-view Clustering via Orthogonalization
2007
Seventh IEEE International Conference on Data Mining (ICDM 2007)
Typical clustering algorithms output a single clustering of the data. However, in real world applications, data can often be interpreted in many different ways; data can have different groupings that are reasonable and interesting from different perspectives. This is especially true for high-dimensional data, where different feature subspaces may reveal different structures of the data. Why commit to one clustering solution while all these alternative clustering views might be interesting to
doi:10.1109/icdm.2007.94
dblp:conf/icdm/CuiFD07
fatcat:7aw4ava33rgy3jjj62jnfzt5o4