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A Novel Intuitionistic Fuzzy Set Generator with Application to Clustering

机译：一种新颖的直觉模糊集生成器及其在聚类中的应用

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摘要

We often have many datasets where hard clustering algorithms do not deliver satisfactory clustering results. It is found that many times fuzzy clustering technique improves the clustering results obtained by hard clustering algorithms. Fuzzy c-means (FCM) is the most prominent fuzzy clustering techniques whose improvement was proposed through the introduction of intuitionistic fuzzy set (IFS) based c-means algorithm. In order to implement IFS based c-means algorithm over a real valued dataset, data points were first converted into IFSs by employing a highly popular technique known as Yager's generating function. The Yager's generating function tunes only the non-membership and hesitancy component of an IFS. Therefore, IFS based c-means algorithm produces compromised clustering results. In this paper, we have generalized the Yager's generating function in such a manner that our IFS generation function tunes all the three components of the IFSs. We have utilized the proposed IFS generation function in two highly used IFS based c-means algorithms of clustering known as intuitionistic fuzzy c-means (IFCM) and Novel intuitionistic fuzzy c-means (Novel-IFCM) algorithms on the UCI datasets. Our results obtained using the proposed function are better than the results obtained using YGF.
机译：我们经常有许多数据集，其中硬群算法不会提供满意的聚类结果。发现许多次模糊聚类技术改善了硬簇算法获得的聚类结果。模糊C-Means（FCM）是最突出的模糊聚类技术，其通过引入基于直觉的C-Means算法来提出的改进。为了通过真实值的数据集实现基于基于的C-Means算法，通过使用称为Yager的生成功能的高流行技术首先将数据点转换为IFSS。 Yager的生成函数仅调整IFS的非成员资格和犹豫组件。因此，如果基于组的C均值算法产生受损的聚类结果。在本文中，我们概括了Yager的生成功能，以便我们的IFS生成函数调整IFSS的所有三个组件。我们已经利用了两个高度使用的IFS的C-Means算法中所提出的IFS生成函数，称为直觉模糊C-MEAR（IFCM）和UCI数据集上的新型直觉模糊C-MERICE（新型IFCM）算法。我们使用所提出的功能获得的结果优于使用YGF获得的结果。

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