快速全局模糊c均值聚类心电信号分类

Y. Koçyigit, I. Kilic
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引用次数: 0

摘要

模糊聚类在解决模式识别和模糊模型识别等领域的问题方面发挥着重要作用。模糊c均值算法是目前应用最广泛的算法之一。它基于对目标函数的优化,对初始条件有响应;该算法通常会得到局部最小值结果。针对上述问题,提出了快速全局模糊c均值聚类算法(FGFCM),该算法是一种不依赖于任何初始条件的增量聚类方法。将该算法应用于心电信号的分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast global Fuzzy C-Means clustering for ECG signal classification
Fuzzy clustering plays an important role in solving problems in the areas of pattern recognition and fuzzy model identification. The Fuzzy C-Means algorithm is one of widely used algorithms. It is based on optimizing an objective function, being responsive to initial conditions; the algorithm usually leads to local minimum results. Aiming at above problem, the fast global Fuzzy C-Means clustering algorithm (FGFCM) has been proposed, which is an incremental approach to clustering, and does not depend on any initial conditions. The algorithm was applied on ECG signals to classification.
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