2006
DOI: 10.1016/j.patcog.2005.09.012
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Iterative shrinking method for clustering problems

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Cited by 303 publications
(115 citation statements)
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“…Regarding clustering quality, the results are consistent with the comparisons made with image data [24]. We expect the results to generalize to other variations of spectral features as well, and to some extent, to other pattern recognition applications.…”
Section: Discussionsupporting
confidence: 84%
See 3 more Smart Citations
“…Regarding clustering quality, the results are consistent with the comparisons made with image data [24]. We expect the results to generalize to other variations of spectral features as well, and to some extent, to other pattern recognition applications.…”
Section: Discussionsupporting
confidence: 84%
“…Usually K-means [52] and expectation-maximization (EM) [5,57] methods have been used, although several better clustering methods exist [24]. This raises the questions of which clustering algorithm should be chosen, and whether the choice between VQ or GMM model matters.…”
Section: Review Of Clustering Methods In Speaker Recognitionmentioning
confidence: 99%
See 2 more Smart Citations
“…For the experiments we used a set of benchmark datasets -based on real-world examples taken from the UCI Machine Learning Repository [18]. In the same time a set of standard synthetic clustering benchmark instances known as S-sets was used [19]. Table I provides the description of the datasets used in the numerical experiments.…”
Section: Experimental Evaluationmentioning
confidence: 99%