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New fuzzy C-means clustering method based on feature-weight and cluster-weight learning

机译：基于特征权重和群集重量学习的新模糊C型聚类方法

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摘要

Among fuzzy clustering methods, fuzzy c-means (FCM) is the most recognized algorithm. In this algorithm, it is assumed that all the features are of equal importance. In real applications, however, the importance of the features are different and there exist some features that are more important than the others. These important features should basically have more effects than the other features in the forming of optimal clusters. The basic FCM algorithm does not support this idea. Also, the FCM algorithm suffers from another problem; the algorithm is very sensitive to initialization, whereas a bad initialization leads to a poor local optima. Some improved versions of FCM have been proposed in the literature, each of which has somehow mitigated the first problem or the second one. In this paper, motivated by these weaknesses of the FCM, the goal is to solve the two problems at the same time. In doing so, an automatic local feature weighting scheme is proposed to properly weight the features of each clusters. And, a cluster weighting process is performed to mitigate the initialization sensitivity of the FCM. Feature weighting and cluster weighting are performed simultaneously and automatically during the clustering process resulting in high quality clusters, regardless of the initial centers. Extensive experiments conducted on a synthetic dataset and 16 real world datasets indicate that the proposed algorithm outperforms the state-of-the-arts algorithms. The convergence proof of the proposed algorithm is also provided. (C) 2019 Elsevier B.V. All rights reserved.
机译：在模糊聚类方法中，模糊C-Manial（FCM）是最识别最识别的算法。在该算法中，假设所有功能都具有相同的重要性。然而，在实际应用中，特征的重要性是不同的，存在一些比其他功能更重要的功能。这些重要特征基本上应具有比形成最佳簇的其他特征更多的效果。基本的FCM算法不支持这个想法。此外，FCM算法遭受了另一个问题;该算法对初始化非常敏感，而初始化差导致较差的本地Optima。在文献中提出了一些改进的FCM版本，其中每个版本都有一些以某种方式减轻了第一个问题或第二个问题。在本文中，由FCM的这些弱点为动机，目标是同时解决这两个问题。在这样做时，提出了一种自动局部特征加权方案来适当地重量每个簇的特征。并且，执行群集加权处理以减轻FCM的初始化灵敏度。特征加权和群集加权在聚类过程中同时和自动执行，导致高质量的群集，无论初始中心如何。在合成数据集和16个真实世界数据集上进行的广泛实验表明所提出的算法优于最先进的算法。还提供了所提出的算法的收敛证明。 （c）2019年Elsevier B.V.保留所有权利。

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