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A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm

机译:一种基于模糊C-均值算法的模糊关系聚类算法

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摘要

In this paper, we show how one can take advantage of the stability and effectiveness of object data clustering algorithms when the data to be clustered are available in the form Of Mutual numerical relationships between pairs of objects. More precisely, we propose a new fuzzy relational algorithm, based on the popular fuzzy C-means (FCM) algorithm, which does not require any particular restriction on the relation matrix. We describe the application of the algorithm to four real and four synthetic data sets, and show that our algorithm performs better than well-known fuzzy relational Clustering algorithms on all these sets.
机译:在本文中,我们展示了当要聚类的数据以对象对之间的相互数值关系的形式可用时,如何利用对象数据聚类算法的稳定性和有效性。更准确地说,我们基于流行的模糊C均值(FCM)算法提出了一种新的模糊关系算法,该算法不需要对关系矩阵进行任何特殊限制。我们描述了该算法在四个实际数据集和四个合成数据集上的应用,并表明我们的算法在所有这些数据集上的性能均优于众所周知的模糊关系聚类算法。

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