论文标题
用于聚类不完整数据集的修改的可能性模糊c均值算法
Modified Possibilistic Fuzzy C-Means Algorithm for Clustering Incomplete Data Sets
论文作者
论文摘要
可能提出了一种可靠的算法，该算法是为了处理两种流行的聚类，模糊c均值（FCM）（FCM）和可能的C-Means（PCM）的弱点。 PFCM算法涉及FCM在处理噪声敏感性和PCM弱点时的弱点。但是，PFCM算法只能应用于群集完整的数据集。因此，在这项研究中，我们提出了可以应用于不完整数据集聚类的PFCM算法的修改。我们将PFCM算法修改为OCSPFCM和NPSPFCM算法，并在三件事上测量了性能：1）精度百分比，2）终止的许多迭代； 3）质心错误。基于结果，两种算法都具有群集不完整的数据集的可能性。但是，对于聚类不完整的数据集，NPSPFCM算法的性能优于OCSPFCM算法。
Possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm has been proposed to deal the weakness of two popular algorithms for clustering, fuzzy c-means (FCM) and possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, the PFCM algorithm can be only applied to cluster complete data sets. Therefore, in this study, we propose a modification of the PFCM algorithm that can be applied to incomplete data sets clustering. We modified the PFCM algorithm to OCSPFCM and NPSPFCM algorithms and measured performance on three things: 1) accuracy percentage, 2) a number of iterations to termination, and 3) centroid errors. Based on the results that both algorithms have the potential for clustering incomplete data sets. However, the performance of the NPSPFCM algorithm is better than the OCSPFCM algorithm for clustering incomplete data sets.
