Missing Clusters Indicate Poor Estimates or Guesses of a Proper Fuzzy Exponent

机译：缺失的类表示适当的模糊指数的估计或猜测不足

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The term 'missing cluster' (MC) is introduced as an undesirable feature of fuzzy partitions. A method for detecting persistent MCs is shown to improve the choice of proper fuzzy parameter values in fuzzy C-means clustering when compared to other methods. The comparison was based on simulated data and gene expression profiles of cancer.

机译：引入术语“缺失群集”（MC）作为模糊分区的不良功能。与其他方法相比，显示了一种用于检测持久MC的方法，可以改善模糊C均值聚类中适当的模糊参数值的选择。比较是基于模拟数据和癌症的基因表达谱。

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来源
《International Workshop on Fuzzy Logic and Applications(WILF 2007); 20070707-10; Camogli(IT)》|2007年|P.161-169|共9页
会议地点 Camogli(IT)
作者
Ulrich Moeller;
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作者单位

Leibniz Institute for Natural Product Research and Infection Biology - Hans Knoell Institute, 07745 Jena, Germany;

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原文格式 PDF
正文语种 eng
中图分类程序设计、软件工程;
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Missing Clusters Indicate Poor Estimates or Guesses of a Proper Fuzzy Exponent

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