论文标题
双曲平滑的模糊聚类
Fuzzy Clustering by Hyperbolic Smoothing
论文作者
论文摘要
我们提出了一种使用平滑数值方法来构建大型数据集模糊簇的新方法。通常会放宽方面的标准，因此在连续的空间上搜索良好的模糊分区，而不是像经典方法\ cite \ cite {hartigan}那样的组合空间。平滑性可以通过使用无限类别的可区分函数，从强烈的非差异问题转换为优化的可区分子问题。为了实现算法，我们使用了统计软件$ r $，并将获得的结果与Bezdek提出的传统模糊$ c $ - 表示方法进行了比较。
We propose a novel method for building fuzzy clusters of large data sets, using a smoothing numerical approach. The usual sum-of-squares criterion is relaxed so the search for good fuzzy partitions is made on a continuous space, rather than a combinatorial space as in classical methods \cite{Hartigan}. The smoothing allows a conversion from a strongly non-differentiable problem into differentiable subproblems of optimization without constraints of low dimension, by using a differentiable function of infinite class. For the implementation of the algorithm we used the statistical software $R$ and the results obtained were compared to the traditional fuzzy $C$--means method, proposed by Bezdek.
