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A Comprehensive Comparison of Different Clustering Methods for Reliability Analysis of Microarray Data
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نویسنده
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Kafieh Rahele ,Mehridehnavi Alireza
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منبع
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journal of medical signals and sensors - 2013 - دوره : 3 - شماره : 1 - صفحه:22 -30
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چکیده
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In this study, we considered some competitive learning methods including hard competitive learning and soft competitive learning with/without fixed network dimensionality for reliability analysis in microarrays. in order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of function optimization), we decided to investigate the abilities of mixture decomposition schemes. therefore, we assert that this study covers the algorithms based on function optimization with particular insistence on different competitive learning methods. the destination is finding the most powerful method according to a pre‑specified criterion determined with numerical methods and matrix similarity measures. furthermore, we should provide an indication showing the intrinsic ability of the dataset to form clusters before we apply a clustering algorithm. therefore, we proposed hopkins statistic as a method for finding the intrinsic ability of a data to be clustered. the results show the remarkable ability of rayleigh mixture model in comparison with other methods in reliability analysis task.
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کلیدواژه
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Clustering ,cluster validity ,microarrays ,reliability analysis
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آدرس
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isfahan university of medical sciences, Medical Image and Signal Processing Research Center, Medical School, Department of Medical Physics and Engineering, ایران, isfahan university of medical sciences, Medical Image and Signal Processing Research Center, Medical School, Department of Medical Physics and Engineering, ایران. University of Waterloo, School of Optometry and Visual Science, Canada
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پست الکترونیکی
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mehri@med.mui.ac.ir
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Authors
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