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首页> 外文期刊>Multimedia Tools and Applications >Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset
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Fuzzy K-means clustering with fast density peak clustering on multivariate kernel estimator with evolutionary multimodal optimization clusters on a large dataset

机译：具有大型数据集的传播多式化优化集群的多变核估计快速密度峰值聚类的模糊k均值聚类

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摘要

Many conventional optimization approaches concentrate more on addressing only one appropriate solution. Thus, these methods were to be utilized often, hence there were no chances of producing the intended solution. Therefore, the issue of multimodal optimization has to be considered. So, to reduce the difficulties by the clustering and further, it followed by the optimization technique. Here, the variety of real-time and artificial techniques are used. Using the FCDP-Fast Clustering with Density Peak, we calculate the density values after determining the center with the help of objective function. Then, the fuzzy clustering is applied to form the clustered groups with the density and center values. Finally, we optimize the data using the CDE-Crowding Differential Evaluation methodology. Performance analysis is then proceeded with some existing methods by using the performance metrics like NM1 and ARI. After validation, it concluded that the proposed method was superior to the existing method.
机译：许多传统优化方法更集中在寻址一个适当的解决方案。因此，通常使用这些方法，因此没有产生预期溶液的机会。因此，必须考虑多式化优化问题。因此，为了降低聚类的困难，然后，它之后是优化技术。这里，使用各种实时和人工技术。使用具有密度峰的FCDP快速聚类，我们在目标函数的帮助下确定中心后计算密度值。然后，应用模糊群集以形成具有密度和中心值的聚类组。最后，我们使用CDE-Crowding差分评估方法优化数据。然后通过使用像NM1和ARI这样的性能指标进行一些现有方法进行性能分析。验证后，它得出结论，该方法优于现有方法。

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