A stability based validity method for fuzzy clustering
A stability based validity method for fuzzy clustering
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DOI：
10.1016/j.patcog.2009.10.001
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发表时间：
2010-04
期刊：
影响因子：
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通讯作者：
M. Falasconi;A. Gutierrez;M. Pardo;G. Sberveglieri;Santiago Marco
中科院分区：
文献类型：
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作者：
M. Falasconi;A. Gutierrez;M. Pardo;G. Sberveglieri;Santiago Marco
聚类分析的一个重要目标是使用客观标准对结果进行内部验证。在这方面特别相关的是估计捕获数据内在结构的聚类的最佳数量。本文提出了一种方法来确定这个最佳数目的基础上评估的模糊划分的稳定性下的自举响应。该方法的特点是首先在合成数据的超参数，如模糊，和空间聚类参数，如特征空间维数，集群的重叠程度，和集群的数量。然后在实验数据集上对该方法进行了验证。此外，所提出的方法的性能进行比较，使用一些传统的模糊有效性规则的基础上的聚类紧凑分离标准。所提出的方法提供了准确和可靠的结果，并提供了更好的泛化能力比经典的方法。
An important goal in cluster analysis is the internal validation of results using an objective criterion. Of particular relevance in this respect is the estimation of the optimum number of clusters capturing the intrinsic structure of your data. This paper proposes a method to determine this optimum number based on the evaluation of fuzzy partition stability under bootstrap resampling. The method is first characterized on synthetic data with respect to hyper-parameters, like the fuzzifier, and spatial clustering parameters, such as feature space dimensionality, clusters degree of overlap, and number of clusters. The method is then validated on experimental datasets. Furthermore, the performance of the proposed method is compared to that obtained using a number of traditional fuzzy validity rules based on the cluster compactness-to-separation criteria. The proposed method provides accurate and reliable results, and offers better generalization capabilities than the classical approaches.