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SM ISO690:2012 AITNOURI, Elmehdi, OUALI, Mohammed. Performance evaluation of clustering techniques for image segmentation. In: Computer Science Journal of Moldova, 2010, nr. 3(54), pp. 271-302. ISSN 1561-4042. |
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Computer Science Journal of Moldova | ||||||
Numărul 3(54) / 2010 / ISSN 1561-4042 /ISSNe 2587-4330 | ||||||
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CZU: 004.93'1+004.932.72 | ||||||
Pag. 271-302 | ||||||
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Rezumat | ||||||
In this paper, we tackle the performance evaluation of two
clustering algorithms: EFC and AIC-based. Both algorithms
face the cluster validation problem, in which they need to estimate the number of components. While EFC algorithm is a
direct method, the AIC-based is a verificative one. For a fair
quantitative evaluation, comparisons are conducted on numerical data and image histograms data are used. We also propose
to use artificial data satisfying the overlapping rate between adjacent components. The artificial data is modeled as a mixture
of univariate normal densities as they are able to approximate a
wide class of continuous densities. |
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Cuvinte-cheie Performance evaluation, probability density function, unsupervised learning, clustering algorithm, univariate normal mixture |
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