@inbook {IOPORT.05342297,
    author       = {Badea, Liviu},
    title        = {Clustering and metaclustering with nonnegative matrix decompositions.},
    year         = {2005},
    booktitle    = {Machine learning: ECML 2005. 16th European conference on machine learning, Porto, Portugal, October 3--7, 2005. Proceedings},
    isbn         = {978-3-540-29243-2},
    pages        = {10-22},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/11564096_7},
    abstract     = {Summary: Although very widely used in unsupervised data mining, most clustering methods are affected by the instability of the resulting clusters w.r.t. the initialization of the algorithm (as e.g. in k-means). Here we show that this problem can be elegantly and efficiently tackled by meta-clustering the clusters produced in several different runs of the algorithm, especially if ``soft'' clustering algorithms (such as Nonnegative Matrix Factorization) are used both at the object- and the meta-level. The essential difference w.r.t. other meta-clustering approaches consists in the fact that our algorithm detects frequently occurring sub-clusters (rather than complete clusters) in the various runs, which allows it to outperform existing algorithms. Additionally, we show how to perform two-way meta-clustering, i.e. take both object and sample dimensions of clusters simultaneously into account, a feature which is essential e.g. for biclustering gene expression data, but has not been considered before.},
    identifier   = {05342297},
}