论文标题
模糊质量C均值集群的新有效指数
A New Validity Index for Fuzzy-Possibilistic C-Means Clustering
论文作者
论文摘要
在某些复杂的数据集中，由于存在嘈杂的数据点和离群值，聚类有效性指数可以在确定最佳簇数量时给出矛盾的结果。本文提出了一个新的有效性指数，用于模糊的c均值聚类，称为模糊质量（FP）索引，该指数在存在形状和密度变化的簇存在下效果很好。此外，像大多数聚类算法一样，FPCM易于某些初始参数。在这方面，除了簇的数量外，FPCM还需要先验选择模糊程度和典型程度。因此，我们提出了一个确定其最佳值的有效程序。已使用几个合成和现实世界数据集评估了所提出的方法。最终计算结果表明，与文献中的几个众所周知的模糊有效性指数相比，该方法的能力和可靠性。此外，为了阐明所提出的方法在实际应用中的能力，在微阵列基因表达数据聚类和医学图像分割中实现了所提出的方法。
In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining the optimal number of clusters. This paper presents a new validity index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic (FP) index, which works well in the presence of clusters that vary in shape and density. Moreover, FPCM like most of the clustering algorithms is susceptible to some initial parameters. In this regard, in addition to the number of clusters, FPCM requires a priori selection of the degree of fuzziness and the degree of typicality. Therefore, we presented an efficient procedure for determining their optimal values. The proposed approach has been evaluated using several synthetic and real-world datasets. Final computational results demonstrate the capabilities and reliability of the proposed approach compared with several well-known fuzzy validity indices in the literature. Furthermore, to clarify the ability of the proposed method in real applications, the proposed method is implemented in microarray gene expression data clustering and medical image segmentation.
