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Enhancement of the classification and reconstruction performance of fuzzy C-means with refinements of prototypes

机译：通过原型改进增强模糊C均值的分类和重构性能

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Owning their abilities to reveal structural relationships in data, fuzzy clustering plays a pivotal role in fuzzy modeling, pattern recognition, and data analysis. As supporting an unsupervised mode of learning, fuzzy clustering, brings about unique opportunities to build a structural backbone of numerous constructs in the areas identified above. A follow-up phase is required when the structural findings developed in the form of fuzzy clusters need some refinements, usually when the clustering results are used afterwards in the model being developed in a supervised mode. Following this general line of thought, in this study we propose a novel approach to optimize the clustering and classification performance of the Fuzzy C-Means (FCM) algorithm. Proceeding with a collection of clusters (information granules) produced by the FCM, we carry out the refinements of the results (in order to improve the representation or classification capabilities of fuzzy clusters) by adjusting a location of the prototypes so that a certain performance index becomes optimized. At this phase, the optimization is carried out in a supervised mode with the aid of Differential Evolution (DE). We propose five different strategies to adjust a location of the prototypes. Experimental studies completed on synthetic data and publicly available real-world data quantify the improvement of the representation and classification abilities of the clustering method. (C) 2016 Elsevier B.V. All rights reserved.
机译：模糊聚类具有揭示数据中结构关系的能力，在模糊建模，模式识别和数据分析中起着关键作用。由于支持无监督的学习模式，因此模糊聚类为在上述区域构建众多构造的结构骨干提供了独特的机会。当以模糊聚类的形式开发的结构发现需要一些改进时，通常是在随后以监督模式开发的模型中使用聚类结果时，需要进行后续阶段。遵循这一总体思路，在本研究中，我们提出了一种新颖的方法来优化模糊C均值（FCM）算法的聚类和分类性能。从FCM产生的聚类（信息颗粒）的集合开始，我们通过调整原型的位置以使特定性能指标对结果进行细化（以提高模糊聚类的表示或分类能力）。变得最优化。在此阶段，借助差分演化（DE）在监督模式下执行优化。我们提出了五种不同的策略来调整原型的位置。对合成数据和可公开获得的真实世界数据完成的实验研究量化了聚类方法表示和分类能力的提高。 （C）2016 Elsevier B.V.保留所有权利。

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